The DOI matches its Crossref record; 78 of the 78 references it cites were found in a registry or an index; 25 of the 33 sources its claims cite were checked for support.
Object detection using YOLO: challenges, architectural successors, datasets and applications
Open accessAbstract
Object detection is one of the predominant and challenging problems in computer vision. Over the decade, with the expeditious evolution of deep learning, researchers have extensively experimented and contributed in the performance enhancement of object detection and related tasks such as object classification, localization, and segmentation using underlying deep models. Broadly, object detectors are classified into two categories viz. two stage and single stage object detectors. Two stage detectors mainly focus on selective region proposals strategy via complex architecture; however, single stage detectors focus on all the spatial region proposals for the possible detection of objects via relatively simpler architecture in one shot. Performance of any object detector is evaluated through detection accuracy and inference time. Generally, the detection accuracy of two stage detectors outperforms single stage object detectors. However, the inference time of single stage detectors is better compared to its counterparts. Moreover, with the advent of YOLO (You Only Look Once) and its architectural successors, the detection accuracy is improving significantly and sometime it is better than two stage detectors. YOLOs are adopted in various applications majorly due to their faster inferences rather than considering detection accuracy. As an example, detection accuracies are 63.4 and 70 for YOLO and Fast-RCNN respectively, however, inference time is around 300 times faster in case of YOLO. In this paper, we present a comprehensive review of single stage object detectors specially YOLOs, regression formulation, their architecture advancements, and performance statistics. Moreover, we summarize the comparative illustration between two stage and single stage object detectors, among different versions of YOLOs, applications based on two stage detectors, and different versions of YOLOs along with the future research directions.
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Claims the paper makes
46 No basis given · 13 Supported · 10 Partly supported · 6 No full text of the sources · 6 Own interpretation · 2 Sources not found · 1 Not checked yet · accuracy not yet measured
Every source the paper cites for this claim was checked and supports it.
In the paper
“Authors demonstrate the capability of shallower networks in the object detection without any explicit need of the accelerators.”
§ Object detection using YOLO: challenges, architectural successors, datasets and applications > Architectural design of YOLOs > YOLO (v1)
What the sources say
- Supportedcites [27] Huang R, Pedoeem J, Chen C (2018) YOLO-LITE: a real-time object detection algorithm optimized for non-GPU computers. In: 2018 IEEE international conference on big data (big data), pp 2503–2510. https://doi.org/10.1109/BigData.2018.8621865 source
The YOLO-LITE Architecture section (p. 3) explicitly identifies fast object detection on non-GPU computers as a capability of shallow networks. The Conclusion (p. 6) reports that YOLO-LITE ran at 21 FPS on a non-GPU computer, supporting the claim that a GPU accelerator was not required. (LLM, conf=0.94)
boosted- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“CSPNets help in rich gradient combination with minimal computation cost [73]”
§ Object detection using YOLO: challenges, architectural successors, datasets and applications > Architectural design of YOLOs > YOLO (v4)
What the sources say
- Supportedcites [73] Wang CY, Mark Liao HY, Wu YH, Chen PY, Hsieh JW, Yeh IH (2020) CSPNet: a new backbone that can enhance learning capability of CNN. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops, pp 390–391 source
The Introduction (p. 2) states that CSPNet was designed to achieve a richer gradient combination while reducing computation, and reports a 10–20% reduction in computation effort when applied to several architectures. This directly supports the paraphrase. (LLM, conf=0.94)
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“YOLO (v2) is known as an immediate successor of YOLO (v1), utilized for detecting objects for around over 9000 categories [54].”
§ Object detection using YOLO: challenges, architectural successors, datasets and applications > Architectural design of YOLOs > YOLO (v2)
What the sources say
- Partly supportedcites [54] Redmon J, Farhadi A (2017) YOLO9000: better, faster, stronger. In: Proceedings of the IEEE conference on computer vision and pattern recognition 2017, pp 7263–7271. https://doi.org/10.48550/arXiv.1612.08242 sourceExperimental — accuracy not yet measured
The Introduction identifies YOLOv2 as an improvement on the original YOLO detector, but attributes detection of over 9000 categories to YOLO9000. The Hierarchical classification section says YOLO9000 uses the base YOLOv2 architecture with additional joint training; it does not assign that category count to YOLOv2 itself. (LLM, conf=0.88)
boosted- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Partly supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“A convolutional neural network predicts the bounding boxes as well as class probabilities for all the objects depicted in an image.”
§ Object detection using YOLO: challenges, architectural successors, datasets and applications > Architectural design of YOLOs
What the sources say
- Supportedcites [56] Redmon J, Divvala S, Girshick R, Farhadi A (2016) You only look once: unified, real-time object detection. In proceedings of the IEEE conference on computer vision and pattern recognition, pp 779-788 source
The paper’s introduction states that a single convolutional network simultaneously predicts multiple bounding boxes and class probabilities. Section 2, “Unified Detection,” describes making predictions across the image and reasoning about its objects. The claim describes YOLO’s prediction task, not a guarantee that it detects every object correctly. (LLM, conf=0.94)
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“The performance of a structured model after deployment is mostly dominated by the model complexity [10].”
§ Object detection using YOLO: challenges, architectural successors, datasets and applications > Convolutional neural networks and Pretrained models
What the sources say
- Partly supportedcites [10] Chen K, Franko K, Sang R (2021) Structured model pruning of convolutional networks on tensor processing units. ArXiv preprint arXiv:210704191. https://doi.org/10.48550/arXiv.2107.04191 sourceExperimental — accuracy not yet measured
The Introduction (p. 1) says deployment is constrained by model size, memory usage, and computational time, and the Results (p. 2) link pruning to improved memory usage and step time. But the paper measures performance during training, not after deployment, and does not establish that model complexity mostly dominates deployed performance. (LLM, conf=0.82)
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Partly supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“Another variant of YOLO, known as Yolo-lite is proposed, specifically designed for real time object detection on a standard non-GPU system [27].”
§ Object detection using YOLO: challenges, architectural successors, datasets and applications > Architectural design of YOLOs > YOLO (v1)
What the sources say
- Supportedcites [27] Huang R, Pedoeem J, Chen C (2018) YOLO-LITE: a real-time object detection algorithm optimized for non-GPU computers. In: 2018 IEEE international conference on big data (big data), pp 2503–2510. https://doi.org/10.1109/BigData.2018.8621865 source
The Introduction (p. 1) says YOLO-LITE uses YOLO as a starting point and aims for real-time object detection on a standard non-GPU computer. The Conclusion (p. 6) reports that it ran at 21 FPS on a non-GPU computer. (LLM, conf=0.95)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Mostly, localization errors are either due to occupancy of background in the predictions and detecting similar objects [45].”
§ Object detection using YOLO: challenges, architectural successors, datasets and applications > Introduction > Challenges in object detection
What the sources say
- Partly supportedcites [45] Liu L, Ouyang W, Wang X, Fieguth P, Chen J, Liu X, Pietikäinen M (2020) Deep learning for generic object detection: a survey. Int J Comput Vis 128(2):261–318. https://doi.org/10.48550/arXiv.1809.02165 sourceExperimental — accuracy not yet measured
Section 9, p. 24 says background pixels included in bounding boxes can affect localization accuracy. But Section 9, p. 25 treats confusion between similar objects as a separate form of error, not a cause of localization error; it instead identifies insufficient overlap and duplicate detections as possible causes. The survey does not say the claimed causes account for most localization errors. (LLM, conf=0.88)
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Partly supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Moreover, they also demonstrate that the presence of batch normalization limits the performance of shallow neural networks in the course of object detection.”
§ Object detection using YOLO: challenges, architectural successors, datasets and applications > Architectural design of YOLOs > YOLO (v1)
What the sources say
- Partly supportedcites [27] Huang R, Pedoeem J, Chen C (2018) YOLO-LITE: a real-time object detection algorithm optimized for non-GPU computers. In: 2018 IEEE international conference on big data (big data), pp 2503–2510. https://doi.org/10.1109/BigData.2018.8621865 sourceExperimental — accuracy not yet measured
The YOLO-LITE Architecture section (p. 3) says batch normalization slows the overall speed of a shallow object-detection network, and the Batch Normalization section (p. 5) explains the added feedforward calculations. The claim broadens this speed-specific finding to overall performance; the paper also notes that batch normalization can improve accuracy (p. 4). (LLM, conf=0.84)
boosteddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Partly supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“Authors of YOLO [56] have reframed the problem of object detection as a regression problem instead of classification problem.”
§ Object detection using YOLO: challenges, architectural successors, datasets and applications > Architectural design of YOLOs
What the sources say
- Supportedcites [56] Redmon J, Divvala S, Girshick R, Farhadi A (2016) You only look once: unified, real-time object detection. In proceedings of the IEEE conference on computer vision and pattern recognition, pp 779-788 source
The paper states on p. 1 that it reframes object detection as a single regression problem, in contrast to prior approaches that repurpose classifiers. It specifies that YOLO predicts bounding-box coordinates and class probabilities, so the claim does not mean YOLO makes no class predictions. (LLM, conf=0.95)
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Supported
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“YOLOs are adopted in various applications majorly due to their faster inferences rather than considering detection accuracy.”
§ Object detection using YOLO: challenges, architectural successors, datasets and applications > Abstract
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Rests on an assumption: Single-stage object detectors generally have faster inference than two-stage detectors.
- Probably not a substantial claim. 51
- It may state two separate claims as one. 30
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Feature Pyramid Network (FPN) [42] is one of the important two stage object detectors, tried to overcome few of the aforementioned challenges successfully by extracting features at multiple scales of an image in the object detection process.”
§ Object detection using YOLO: challenges, architectural successors, datasets and applications > Two stage object detection > R-CNN and successors
What the sources say
- Partly supportedcites [42] Lin TY, Dollár P, Girshick R, He K, Hariharan B, Belongie S (2017) Feature pyramid networks for object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2117–2125 sourceExperimental — accuracy not yet measured
The Abstract (p. 1) and Introduction (p. 2) support that FPN builds multi-scale features and improves object detection when used with Faster R-CNN. They describe FPN as a generic feature extractor used in detection systems, however, rather than as a two-stage detector in its own right. (LLM, conf=0.86)
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
- Partly supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“This version is faster and more accurate than all the previous versions.”
§ Object detection using YOLO: challenges, architectural successors, datasets and applications > Architectural design of YOLOs > YOLO (v4)
What the sources say
- Partly supportedcites [5] Bochkovskiy A, Wang CY, Liao HY (2020) YOLOv4: optimal speed and accuracy of object detection. arXiv preprint arXiv:2004.10934 sourceExperimental — accuracy not yet measured
The Introduction (p. 1) says YOLOv4 improves YOLOv3’s accuracy (AP) and speed (FPS) by 10% and 12%, respectively. The cited passages do not establish that it is faster and more accurate than every previous YOLO version. (LLM, conf=0.86)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Partly supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“A novel OCR system is developed, named as Rosetta, wherein faster-RCNN is utilized for detecting the text characters from millions of Facebook images, however, fully convolutional CNN is employed for generating the lexicon free transcription of each word [6].”
§ Object detection using YOLO: challenges, architectural successors, datasets and applications > Two stage object detection > Applications of R-CNN and successors
What the sources say
- Partly supportedcites [6] Borisyuk F, Gordo A, Sivakumar V (2018) Rosetta: large scale system for text detection and recognition in images. In proceedings of the 24th ACM SIGKDD international conference on knowledge discovery data mining pp 71-79 sourceExperimental — accuracy not yet measured
The Introduction (p. 1) describes Rosetta as a Facebook OCR system that uses Faster-RCNN to detect individual words and a fully convolutional CNN to produce a lexicon-free transcription of each word. The claim is inaccurate in saying Faster-RCNN detects text characters; the paper assigns character recognition to the second stage. (LLM, conf=0.90)
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
- Partly supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“There are numerous other algorithms that have been introduced in recent past such as Single Shot Detector (SSD) [43], Deconvolution Single Shot Detector (DSSD) [16], RetinaNet [41], M2Det [86], RefineDet++ [85], are based on single stage object detection.”
§ Object detection using YOLO: challenges, architectural successors, datasets and applications > Introduction > Stages in object detection
What the sources say
- Supportedcites [43] Liu W, Anguelov D, Erhan D, Szegedy C, Reed S, Fu CY, Berg AC (2016) Ssd: single shot multibox detector. European Conf Comput Vis 2016:21–37. https://doi.org/10.48550/arXiv.1512.02325 sourceExperimental — accuracy not yet measured
The citation to this paper is attached specifically to SSD, not to the other named algorithms. The Conclusions (p. 15) identify SSD as a single-shot object detector, supporting its inclusion as a single-stage detection algorithm. (LLM, conf=0.94)
- Supportedcites [16] Fu CY, Liu W, Ranga A, Tyagi A, Berg AC (2017) Dssd: Deconvolutional single shot detector. arXiv preprint arXiv:1701.06659 source
The citation is attached to DSSD, not to the other separately cited algorithms. The Introduction (p. 1) identifies this paper’s proposed system as the deconvolutional single shot detector, built by augmenting SSD. (LLM, conf=0.94)
- Supportedcites [41] Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988. https://doi.org/10.48550/arXiv.1708.02002 sourceExperimental — accuracy not yet measured
The Introduction (p. 1) presents RetinaNet as a one-stage object detector, and the RetinaNet Detector section (p. 4) describes its design for one-stage detection. Because this citation is attached to RetinaNet, it need not substantiate the other named algorithms. (LLM, conf=0.95)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“According to [65], the inception modules contain three different sizes of filters viz. 1 × 1, 3 × 3, and 5 × 5 for capturing different sized patterns.”
§ Object detection using YOLO: challenges, architectural successors, datasets and applications > Convolutional neural networks and Pretrained models > GoogLeNet
What the sources say
- Supportedcites [65] Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1–9. https://doi.org/10.48550/arXiv.1409.4842 source
Architectural Details (p. 4) says the Inception architecture uses 1 × 1, 3 × 3, and 5 × 5 filters. The same section (p. 5) explains that this design processes visual information at various scales, supporting the paraphrase about capturing different-sized patterns. (LLM, conf=0.94)
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“It has resulted in a decrease in number of parameters, so as the model complexity [39].”
§ Object detection using YOLO: challenges, architectural successors, datasets and applications > Convolutional neural networks and Pretrained models > Network in network
What the sources say
- Supportedcites [39] Lin M, Chen Q, Yan S (2013) Network in network. arXiv preprint arXiv:1312.4400. https://doi.org/10.48550/arXiv.1312.4400 source
Section 3.2 (p. 4) says global average pooling replaces fully connected layers and has no parameters to optimize at that layer. Section 4.6 (p. 7) contrasts its fixed transformation with the dense, learned transformation of a fully connected layer, supporting the claim that this replacement reduces parameters and model complexity. (LLM, conf=0.87)
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
- Supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“The authors proposed this architecture in two flavors viz. VGG19 and VGG16, consisting of 19 and 16 layers in the deep neural networks respectively [64].”
§ Object detection using YOLO: challenges, architectural successors, datasets and applications > Convolutional neural networks and Pretrained models > VGG
What the sources say
- Supportedcites [64] Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409. https://doi.org/10.48550/arXiv.1409.1556 source
The paper calls its models the 16-layer Net-D and the 19-layer Net-E in “Generalisation of Very Deep Features” (p. 13). This supports the attributed layer counts for VGG16 and VGG19. (LLM, conf=0.93)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
- Supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“The number of layers is increased to 34 which is almost doubled in comparison with VGG19 [64].”
§ Object detection using YOLO: challenges, architectural successors, datasets and applications > Convolutional neural networks and Pretrained models > ResNet
What the sources say
- Supportedcites [64] Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409. https://doi.org/10.48550/arXiv.1409.1556 source
Table 1 in §2.3 (p. 3) identifies VGG configuration E as having 19 weight layers, supporting the VGG19 comparison for which this citation is offered. The cited paper does not establish ResNet’s 34-layer count; that is not the VGG-specific point attributed to it here. (LLM, conf=0.88)
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
- Supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“The third version of YOLO tries to overcome aforementioned drawbacks and provides an efficient way for detecting objects with an improved performance while trained and evaluated on the COCO dataset [55].”
§ Object detection using YOLO: challenges, architectural successors, datasets and applications > Architectural design of YOLOs > YOLO (v3)
What the sources say
- Supportedcites [55] Redmon J, Farhadi A (2018) Yolov3: an incremental improvement. arXiv preprint arXiv:1804.02767 source
Section 2.3 (p. 2) describes YOLOv3 experiments with COCO, and Section 3 (p. 3) evaluates its detection performance using COCO metrics. The Abstract (p. 1) says the new network was trained and is more accurate than the previous version while remaining fast. The improvement is metric-dependent: Section 3 notes that YOLOv3 trails RetinaNet on COCO’s average AP metric. (LLM, conf=0.87)
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“They proposed smart factorization in the convolution operation, specifically, 3 × 3 filters are used throughout the entire architecture to introduce model consistency and to reduce the number of parameters.”
§ Object detection using YOLO: challenges, architectural successors, datasets and applications > Convolutional neural networks and Pretrained models > VGG
What the sources say
- Partly supportedcites [64] Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409. https://doi.org/10.48550/arXiv.1409.1556 sourceExperimental — accuracy not yet measured
The Discussion (p. 2) says the networks use small 3 × 3 receptive fields throughout and explains that stacking them uses fewer parameters than a single 7 × 7 layer. The Architecture section (p. 2) also notes a configuration with 1 × 1 filters, and the paper does not identify “model consistency” as a reason for the design. (LLM, conf=0.85)
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Partly supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“Object detection algorithms in deep learning need larger datasets for computation, labor intensive approaches for annotations, and powerful computational resources for processing [45].”
§ Object detection using YOLO: challenges, architectural successors, datasets and applications > Introduction > Challenges in object detection
What the sources say
- Supportedcites [45] Liu L, Ouyang W, Wang X, Fieguth P, Chen J, Liu X, Pietikäinen M (2020) Deep learning for generic object detection: a survey. Int J Comput Vis 128(2):261–318. https://doi.org/10.48550/arXiv.1809.02165 sourceExperimental — accuracy not yet measured
Section 2.3 (p. 5) says deep detectors rely heavily on vast training data and high-capability GPUs, and that accurate annotations are labor-intensive to obtain. This supports all three parts of the attributed claim. (LLM, conf=0.94)
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Additionally, SoftMax is used as an activation function in the last layer and (1 × 1) convolution filters are placed in a multilayer perceptron after convolutional layer resulting in dimensionality reduction [23].”
§ Object detection using YOLO: challenges, architectural successors, datasets and applications > Convolutional neural networks and Pretrained models > Network in network
What the sources say
- Partly supportedcites [23] Gu J, Wang Z, Kuen J, Ma L, Shahroudy A, Shuai B, Liu T, Wang X, Wang G, Cai J, Chen T (2018) Recent advances in convolutional neural networks. Pattern Recogn 77:354–377. https://doi.org/10.1016/j.patcog.2017.10.013 sourceExperimental — accuracy not yet measured
Section 3.1.4 (p. 5) says Network in Network places 1 × 1 convolutions after a traditional convolutional layer in its mlpconv layer and feeds the final pooled output into softmax. It does not say those convolutions reduce dimensionality; Section 3.1.5 (p. 5) assigns that role to 1 × 1 convolutions in the Inception module. (LLM, conf=0.90)
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
- Partly supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Real time object detection and tracking is implemented and benchmarking results are obtained using embedded hardware such as Jetson TX/AGX Xavier and Intel Neural Compute Stick wherein RCNN is employed on the embedded hardware for object detection [26].”
§ Object detection using YOLO: challenges, architectural successors, datasets and applications > Two stage object detection > Applications of R-CNN and successors
What the sources say
- Partly supportedcites [26] Hossain S, Lee DJ (2019) Deep learning-based real-time multiple-object detection and tracking from aerial imagery via a flying robot with GPU-based embedded devices. Sensors 19(15):3371 sourceExperimental — accuracy not yet measured
The Abstract and §7 describe embedded detection and tracking with benchmark results for Jetson devices and systems using a Neural Compute Stick. Section 2.2.1 names R-CNN among the detection algorithms for the Xavier system, and §5.1 reports that Faster R-CNN was accurate but had a very low performance rate. The cited passages do not establish that R-CNN ran on the Neural Compute Stick or that its detection achieved real-time performance; §5.6 reports stick-based FPS results without identifying R-CNN. (LLM, conf=0.83)
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Partly supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“It is 20 times deeper than AlexNet and 8 times deeper than VGG [23].”
§ Object detection using YOLO: challenges, architectural successors, datasets and applications > Convolutional neural networks and Pretrained models > ResNet
What the sources say
- Supportedcites [23] Gu J, Wang Z, Kuen J, Ma L, Shahroudy A, Shuai B, Liu T, Wang X, Wang G, Cai J, Chen T (2018) Recent advances in convolutional neural networks. Pattern Recogn 77:354–377. https://doi.org/10.1016/j.patcog.2017.10.013 source
The opening passage (p. 1) explicitly says ResNet is about 20 times deeper than AlexNet and 8 times deeper than VGGNet. The citing sentence conveys those same comparisons, though it omits “about.” (LLM, conf=0.94)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
- Supported
In the paper
“Fast R-CNN [20] achieves a significant improvement in the model training time and inference time. Moreover, it also observes an increase in the performance matrix of the object detection i.e., mAP.”
§ Object detection using YOLO: challenges, architectural successors, datasets and applications > Two stage object detection > R-CNN and successors
What the sources say
- Not checked — no full text of the sourcecites [20] Girshick R (2015) Fast r-cnn. In proceedings of the IEEE international conference on computer vision 2015, pp 1440–1448 source
plaindirect- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“YOLO can be seen as giving a tough fight to not only two staged detectors but previous single staged detectors also in terms of accuracy and inference time.”
§ Object detection using YOLO: challenges, architectural successors, datasets and applications > Introduction > Stages in object detection
plaincorrelational- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
- Rests on an assumption: Single-stage object detectors generally have faster inference than two-stage detectors.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Moreover, with the advent of YOLO (You Only Look Once) and its architectural successors, the detection accuracy is improving significantly and sometime it is better than two stage detectors.”
§ Object detection using YOLO: challenges, architectural successors, datasets and applications > Abstract
hedgeddirect- Asserted: passes — The authors put it forward as true.
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- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: unclear — It is unclear why the paper says it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“In the experimentational setup for object detection on Pascal VOC 2007, Fast-RCNN and Faster-RCNN are 25 and 250 times faster respectively compared to traditional RCNN with almost similar mAP of around 66.”
§ Object detection using YOLO: challenges, architectural successors, datasets and applications > Two stage object detection > R-CNN and successors
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“A variant of YOLO with lesser model complexity known as Fast YOLO is proposed for faster detection of objects. It has 9 convolutional layers with comparatively lesser filters in those layers.”
§ Object detection using YOLO: challenges, architectural successors, datasets and applications > Architectural design of YOLOs > YOLO (v1)
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: fails — The paper’s conclusions may not depend on it.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“Unfortunately, the network turned out to be an over parameterized network leading to large training error [25].”
§ Object detection using YOLO: challenges, architectural successors, datasets and applications > Convolutional neural networks and Pretrained models > ResNet
What the sources say
- Not checked — no full text of the sourcecites [25] He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In proceedings of the IEEE conference on computer vision and pattern recognition, pp 770-778 source
plaindirect- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Confidence score (cs) is computed for each bounding box per grid by multiplying pc with Intersection over Union (IoU) between the ground-truth and predicted-bounding-box.”
§ Object detection using YOLO: challenges, architectural successors, datasets and applications > Architectural design of YOLOs
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“YOLO is not the first algorithm that uses Single Shot Detector (SSD) for object detection.”
§ Object detection using YOLO: challenges, architectural successors, datasets and applications > Introduction > Stages in object detection
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
In the paper
“CSPDarknet53, a network with 29 convolution layers with 3 × 3 filters and approximately 27.6 million parameters, is chosen as backbone that outperformed the remaining architectures”
§ Object detection using YOLO: challenges, architectural successors, datasets and applications > Architectural design of YOLOs > YOLO (v4)
plaindirect- Asserted: passes — The authors put it forward as true.
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- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“This architecture is based on Darkent-53 framework, a network that uses 3 × 3 and 1 × 1 convolutional filter along with some shortcut connections, significantly larger and powerful architecture with 53 convolutional layers.”
§ Object detection using YOLO: challenges, architectural successors, datasets and applications > Architectural design of YOLOs > YOLO (v3)
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: fails — The paper’s conclusions may not depend on it.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“It is considered as one of the most common choices in production only because of its simple architectural design, low complexity, and easy implementation.”
§ Object detection using YOLO: challenges, architectural successors, datasets and applications > Introduction > Stages in object detection
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“the image/frame is divided into S × S grid cells, each grid cell predicts B bounding boxes along with their positions and dimensions, probability of an object in the underlying grid, and conditional class probabilities.”
§ Object detection using YOLO: challenges, architectural successors, datasets and applications > Architectural design of YOLOs
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: fails — The paper’s conclusions may not depend on it.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“In addition, ResNet152 has a total of 25.6 million parameters whereas ResNet110 has 1.7 million parameters [31].”
§ Object detection using YOLO: challenges, architectural successors, datasets and applications > Convolutional neural networks and Pretrained models > ResNet
What the sources say
- Not checked — no full text of the sourcecites [31] Khan A, Sohail A, Zahoora U, Qureshi AS (2020) A survey of the recent architectures of deep convolutional neural networks. Artif Intell Rev 53(8):5455–5516. https://doi.org/10.1007/s10462-018-9633-3 source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“It generates feature maps at three different scales. Specifically, it down-samples the input at 3 different scales by a factor of 32, 16, and 8.”
§ Object detection using YOLO: challenges, architectural successors, datasets and applications > Architectural design of YOLOs > YOLO (v3)
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Lighter the model, faster the inferences and vice versa, generating a trade-off between the efficacy and performance matrix of a deployed structured model on tensor processing unit (TPU).”
§ Object detection using YOLO: challenges, architectural successors, datasets and applications > Convolutional neural networks and Pretrained models
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Bounding boxes are the approximations of the ground-truth. Generally, background pixels are also included during predictions, this affects the accuracy of the algorithm.”
§ Object detection using YOLO: challenges, architectural successors, datasets and applications > Introduction > Challenges in object detection
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“After exclusion of fully connected layers, YOLO (v2) can now operate on any dimension ranging from 320 × 320 to 608 × 608.”
§ Object detection using YOLO: challenges, architectural successors, datasets and applications > Architectural design of YOLOs > YOLO (v2)
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Here three layers of mlpconv are stacked followed by a Global Average Pooling (GAP) layer [12].”
§ Object detection using YOLO: challenges, architectural successors, datasets and applications > Convolutional neural networks and Pretrained models > Network in network
What the sources say
- Not matchedcites [12] Cook A (2017) Global average pooling layers for object localization. https://alexisbcook.github.io/2017/globalaverage-poolinglayers-for-object-localization/. Accessed 19 Aug 2019
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: fails — The paper’s conclusions may not depend on it.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
In the paper
“This architecture is a deep network with 22 layers but has 12 times less parameters than AlexNet architecture, consisting of 4 million parameters approximately [31].”
§ Object detection using YOLO: challenges, architectural successors, datasets and applications > Convolutional neural networks and Pretrained models > GoogLeNet
What the sources say
- Not checked — no full text of the sourcecites [31] Khan A, Sohail A, Zahoora U, Qureshi AS (2020) A survey of the recent architectures of deep convolutional neural networks. Artif Intell Rev 53(8):5455–5516. https://doi.org/10.1007/s10462-018-9633-3 source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The architecture of YOLO is inspired by the GoogLeNet architecture.”
§ Object detection using YOLO: challenges, architectural successors, datasets and applications > Architectural design of YOLOs > YOLO (v1)
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“In addition, Path Aggregation Network (PAN) is preferred over Feature Pyramid Network (FPN) where PAN is a bottom-up path augmentation that uses Adaptive feature pooling which works similar to FPN.”
§ Object detection using YOLO: challenges, architectural successors, datasets and applications > Architectural design of YOLOs > YOLO (v4)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“It records much better performance than any of the previous architectures on ImageNet dataset.”
§ Object detection using YOLO: challenges, architectural successors, datasets and applications > Convolutional neural networks and Pretrained models > GoogLeNet
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“In YOLO, we have 24 convolution layers followed by 2 fully connected layers as shown in Fig. 18.”
§ Object detection using YOLO: challenges, architectural successors, datasets and applications > Architectural design of YOLOs > YOLO (v1)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“It uses darknet-19 framework consisting of 19 convolutional layers and 5 max pooling layers, as illustrated in Fig. 19.”
§ Object detection using YOLO: challenges, architectural successors, datasets and applications > Architectural design of YOLOs > YOLO (v2)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“YOLOs are performing significantly better in comparison with their counterpart two stage object detectors in terms of detection accuracy and inference time.”
§ Object detection using YOLO: challenges, architectural successors, datasets and applications > Summarization and future research directions
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The second criteria for discarding the less relevant bounding boxes is known as non max suppression which is further based upon the IoU.”
§ Object detection using YOLO: challenges, architectural successors, datasets and applications > Architectural design of YOLOs
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Subsequently, the concept of identity mapping is introduced in [11]. A skip or residual connection of length 2 is proposed as shown in Fig. 13.”
§ Object detection using YOLO: challenges, architectural successors, datasets and applications > Convolutional neural networks and Pretrained models > ResNet
What the sources say
- Not checked — no full text of the sourcecites [11] Choi H, Ryu S, Kim H (2018) Short-term load forecasting based on ResNet and LSTM. In IEEE international conference on communications, control, and computing Technologies for Smart Grids (SmartGridComm), pp 1-6 source
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The improvised architecture is known as VGG, achieved a top-5 accuracy of 92.7% for an object detection task on the test dataset of ImageNet, a standard dataset containing around 14 million images belonging to 1 k classes.”
§ Object detection using YOLO: challenges, architectural successors, datasets and applications > Convolutional neural networks and Pretrained models > VGG
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Smaller filters result in fewer parameters and are capable of identifying smaller objects more effectively.”
§ Object detection using YOLO: challenges, architectural successors, datasets and applications > Convolutional neural networks and Pretrained models > VGG
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Though deep learning models outperform traditional machine learning approaches by a great margin, they demonstrate poor performance while evaluating on the datasets with fewer instances.”
§ Object detection using YOLO: challenges, architectural successors, datasets and applications > Introduction > Challenges in object detection
boosteddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: unclear — It is unclear why the paper says it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Due to scaling inefficacy in deep neural networks, CNN is widely adopted to capture the spatial and contextual information with fewer parameters.”
§ Object detection using YOLO: challenges, architectural successors, datasets and applications > Convolutional neural networks and Pretrained models
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“A group of researchers from Google developed an application using computer vision, machine learning, and Google’s Knowledge Graph called as Google Lens [22] wherein a region proposal network (RPN) is utilized to detect the character level bounding boxes for text recognition.”
§ Object detection using YOLO: challenges, architectural successors, datasets and applications > Two stage object detection > Applications of R-CNN and successors
What the sources say
- Not matchedcites [22] Google Lens – Wikipedia (n.d.), https://en.wikipedia.org/wiki/Google_Lens. Accessed 06 Aug 2020
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plaindirect- Asserted: passes — The authors put it forward as true.
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- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“An approximate increase of 7% in recall is observed by the usage of convolution with anchor boxes, however, 0.3% reduction in mAP is also noticed.”
§ Object detection using YOLO: challenges, architectural successors, datasets and applications > Architectural design of YOLOs > YOLO (v2)
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“however, this version struggles in detection of smaller sized objects.”
§ Object detection using YOLO: challenges, architectural successors, datasets and applications > Architectural design of YOLOs > YOLO (v2)
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
In the paper
“With increase in depth of network by series of convolutions, pooling layers and fully connected layers, it has resulted in 138 M parameters approximately [31].”
§ Object detection using YOLO: challenges, architectural successors, datasets and applications > Convolutional neural networks and Pretrained models > VGG
What the sources say
- Not checked — no full text of the sourcecites [31] Khan A, Sohail A, Zahoora U, Qureshi AS (2020) A survey of the recent architectures of deep convolutional neural networks. Artif Intell Rev 53(8):5455–5516. https://doi.org/10.1007/s10462-018-9633-3 source
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Large localization error and lower recall in comparison with two stage object detectors may be considered as the two significant drawbacks of this version of YOLO.”
§ Object detection using YOLO: challenges, architectural successors, datasets and applications > Architectural design of YOLOs > YOLO (v1)
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Authors introduced the problem of model degradation i.e. model accuracy increases with an increase in the model depth to a certain extent only.”
§ Object detection using YOLO: challenges, architectural successors, datasets and applications > Convolutional neural networks and Pretrained models > ResNet
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“As this version outperforms for smaller sized objects, however, suffers in producing accurate results for medium and large sized objects.”
§ Object detection using YOLO: challenges, architectural successors, datasets and applications > Architectural design of YOLOs > YOLO (v3)
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“A grid cell predicts (B × 5 + n) values, where B is the number of bounding boxes per grid cell. The output tensor shape would be S × S×(B × 5 + n)”
§ Object detection using YOLO: challenges, architectural successors, datasets and applications > Architectural design of YOLOs
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“In order to overcome the problem of vanishing gradients, auxiliary loss is utilized by introducing an intermediate SoftMax classifiers during model training.”
§ Object detection using YOLO: challenges, architectural successors, datasets and applications > Convolutional neural networks and Pretrained models > GoogLeNet
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Moreover, YOLOs make comparatively fewer errors in the identification of the background”
§ Object detection using YOLO: challenges, architectural successors, datasets and applications > Summarization and future research directions
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The combination of YOLO and Fast-RCNN outperform to the each of these standalone architecture on Pascal VOC dataset.”
§ Object detection using YOLO: challenges, architectural successors, datasets and applications > Summarization and future research directions
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Single stage object detectors are mainly designed for real time object detection; however, they are lagging in the performance metrics to the two stage object detectors.”
§ Object detection using YOLO: challenges, architectural successors, datasets and applications > Summarization and future research directions
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: unclear — It is unclear why the paper says it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“YOLO-v4 is also an attempt to better deal with the trade-off between the accuracy and speed of the model by introducing several universal optimization features of CNNs.”
§ Object detection using YOLO: challenges, architectural successors, datasets and applications > Summarization and future research directions
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Generally, deep models take huge amount of time for model training due to the large dataset size and model complexity”
§ Object detection using YOLO: challenges, architectural successors, datasets and applications > Summarization and future research directions
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“With the help of multi scale training, YOLO-v2 offers better detection on varying sized objects with an easy trade-off between the model performance and model inferences.”
§ Object detection using YOLO: challenges, architectural successors, datasets and applications > Summarization and future research directions
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“YOLOs are being rigorously employed in various real time object tracking such as self-driving cars.”
§ Object detection using YOLO: challenges, architectural successors, datasets and applications > Summarization and future research directions
boostedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Models trained for one particular task may not perform well on other similar tasks, resulting non-generalizability of the model for the data it has not seen before.”
§ Object detection using YOLO: challenges, architectural successors, datasets and applications > Summarization and future research directions
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“YOLO-v3 is better detector, fast, and accurate, necked by feature pyramid network.”
§ Object detection using YOLO: challenges, architectural successors, datasets and applications > Summarization and future research directions
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
The paper relies on this without support.
Most existing reviews and surveys cover two-stage object detection.
“Most of the reviews and surveys cover the two stage object detection algorithms.”
The authors state this as an assumption.
This is the first review covering single-stage object detection using YOLOs.
“To the best of our knowledge, this is the first review that covers the single stage object detection using specially YOLOs.”
The paper relies on this without support.
Single-stage object detectors generally have faster inference than two-stage detectors.
“However, the inference time of single stage detectors is better compared to its counterparts.”
The paper relies on this without support.
Most existing object detection techniques are restricted to classification for a limited number of categories.
“Most of the existing object detection techniques are restricted to classification for limited categories.”
The paper relies on this without support.
The scarcity of labelled data for object detection is the reason existing techniques are limited to few categories.
“This is certainly due to scarcity of labelled data for object detection task.”
The paper relies on this without support.
Random initialization is the biggest hurdle in predicting genuine offsets.
“Random initialization is considered as the biggest hurdles in predicting genuine offsets.”
The paper relies on this without support.
Better shaped anchor boxes provide a quick start and improve model training and performance.
“Better shaped anchor boxes may provide quick start and offers improved model training and performance.”
The paper relies on this without support.
The threshold for discarding boxes is usually 0.5 in most object detection algorithms.
“Usually, this threshold is taken as 0.5 in most of the object detection algorithms”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The cited source says what the claim attributes to it.
In the citing paper
“Machine learning models, particularly those based on deep learning architectures like YOLO, have demonstrated significant advancements in object detection capabilities [93].”
From A Review of Passenger Counting in Public Transport Concepts with Solution Proposal Based on Image Processing and Machine Learning (2024) · cites this paper as [93]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Object detection using YOLO: challenges, architectural successors, datasets and applications > Architectural design of YOLOs > YOLO (v3)
“Conclusively different feature maps viz. 52 × 52, 13 × 13, and 26 × 26 are utilized for detecting large, smaller, and medium-sized objects respectively.”
§ Object detection using YOLO: challenges, architectural successors, datasets and applications > Architectural design of YOLOs > YOLO (v3)
“Finally, the 106th layer is involved in the detection using a 52 × 52 feature map after applying a stride of 8. Following the same process, adding few convolutions to the 91st layer and combining with the 36th layer using (1 × 1) kernel, the down-sampled feature maps are concatenated to Up-sampled feature maps at different places to extract fine-grained features for detecting smaller objects of various dimensions.”
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Object detection using YOLO: challenges, architectural successors, datasets and applications > Two stage object detection > R-CNN and successors
“Fast R-CNN [20] achieves a significant improvement in the model training time and inference time. Moreover, it also observes an increase in the performance matrix of the object detection i.e., mAP. Single stage object detection is realized with the help of multi-task loss function”
§ Object detection using YOLO: challenges, architectural successors, datasets and applications > Summarization and future research directions
“In this paper, we explored two stage object detectors viz. RCNN, Fast-RCNN, and Faster-RCNN along with their important applications.”
Citations
Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474 · cited 3×
“20 Skip connections in the ResNet Module [25] aforementioned drawbacks and provides an efficient way for detecting objects with an improved performance while trained and evaluated on the COCO dataset [55].”
BackgroundVery Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 114,148 · cited 2×
“VGG19 and VGG16, consisting of 19 and 16 layers in the deep neural networks respectively [64].”
BackgroundResultYou Only Look Once: Unified, Real-Time Object Detection
Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.91 · Cited by 53,803 · cited 2×
“18 YOLO architecture for object detection and localization [56] predicts there ’ s an object.”
BackgroundRich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation
Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.81 · Cited by 32,684 · cited 1×
“R-CNN is a region based convolutional neural network object detection algorithm proposed by Ross Girshick [21].”
MethodFeature Pyramid Networks for Object Detection
Lin, T.-Y., Dollar, P., Girshick, R., He, K., Hariharan, B., & Belongie, S. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.106 · Cited by 30,939 · cited 6×
“It incorporates heuristics like residual blocks, skip connections, and up-sampling similar to FPN.”
BackgroundInfluentialGirshick, R. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.169 · Cited by 28,105
SSD: Single Shot MultiBox Detector
Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., & Berg, A. C. (2015) · arXiv · doi:10.48550/arxiv.1512.02325 · Cited by 22,443 · cited 1×
“There are numerous other algorithms that have been introduced in recent past such as Single Shot Detector (SSD) [43], Deconvolution Single Shot Detector (DSSD) [16], RetinaNet [41], M2Det [86], RefineDet++ [85], are based on single stage object detection.”
MethodSSD: Single Shot MultiBox Detector
Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., & Berg, A. C. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46448-0_2 · Cited by 22,128
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“Well known machine learning approaches for object detection include Viola-Jones object detection framework [70] and Histogram of Oriented Gradients (HOG).”
Method
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- Morris T (2004) Computer Vision and Image Processing, Palgrave Macmillan Ltd, 1st edition, pp 1–320
- Ren S, He K, Girshick R, Sun J (2015) Faster r-cnn: towards real-time object detection with region proposal networks. Adv Neural Inform Process Syst:91–99
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Cited by
Sorted by citations
Items 601–700 of 1,537
Tsukada, S., Ogawa, H., Saito, M., et al. (2024) · Arthroplasty Today · doi:10.1016/j.artd.2024.101439 · Cited by 3
“YOLO is an AI algorithm designed for computer vision tasks [8].”
Agritania, M. M., & Isnaini, M. M. (2025) · Procedia CIRP · doi:10.1016/j.procir.2025.01.023 · Cited by 3
Yang, Y., Wang, X., Liu, Z., Huang, M., Sun, S., & Zhu, Q. (2023) · Intelligent Data Analysis · doi:10.3233/ida-220449 · Cited by 4
Smart and Secure Vehicle Parking System to Avoid Theft Using Deep Image Recognition
Ramzan, S., Iqbal, M. J. I. M. J., Arslan, F., et al. (2024) · Journal of Innovative Computing and Emerging Technologies · doi:10.56536/jicet.v4i1.105 · Cited by 3
Towards personalized HVAC: A non-contact human thermal sensation monitoring and regulation system
Li, K., Luo, Y., Shen, Y., et al. (2025) · Energy and Buildings · doi:10.1016/j.enbuild.2025.116649 · Cited by 3
Anuntavachakorn, P., Pawarana, P., Wongvorachan, T., Thampanichwat, C., & Bunyarittikit, S. (2025) · Buildings · doi:10.3390/buildings15152659 · Cited by 3
Jaihuni, M., Zhao, Y., Gan, H., et al. (2025) · AgriEngineering · doi:10.3390/agriengineering7050133 · Cited by 3
Devarshi, P., Yash, C., Patel, M. B., et al. (2025) · Procedia Computer Science · doi:10.1016/j.procs.2025.04.281 · Cited by 3
Deep Learning-Based Object Detection Techniques for Self-Driving Cars: an in-Depth Analysis
Isong, B. E., Mashego, T., Moemi, T. J., & Dladlu, N. (2023) · International Conference on Electrical, Computer and Energy Technologies (ICECET) · doi:10.1109/icecet58911.2023.10389376 · Cited by 3
Guan, Z., Zhang, R., & Lin, M. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.129212 · Cited by 3
Arabic-Latin Scene Text Detection based on YOLO Models
Turki, H., Elleuch, M., Kherallah, M., et al. (2023) · International Conference on Innovations in Intelligent Systems and Applications (INISTA) · doi:10.1109/inista59065.2023.10310530 · Cited by 3
A Deep Learning-Based Algorithm for Ceramic Product Defect Detection
Diao, J., Wei, H., Zhou, Y., et al. (2025) · Applied Sciences · doi:10.3390/app15126641 · Cited by 3
“These network architectures, such as the classic Faster R-CNN [7], YOLO [8] series, and SSD [9], can automatically learn low-level texture features, mid-level shape features, and high-level semantic features from images, thereby capturing the essential characteristics of targets under varying…”
Chong, J. W. R., Khoo, K. S., Ting, H.-Y., Koji, I., Ma, Z., & Show, P. L. (2026) · Ecological Informatics · doi:10.1016/j.ecoinf.2026.103752 · Cited by 3
Abdelaziz, M., & Sullivan, A. L. (2025) · International Journal of Wildland Fire · doi:10.1071/wf25153 · Cited by 3
A YOLO-Based Framework for Road Sign Detection and Recognition in the Context of Bangladesh
Shuvo, M. I. M., Dey, A., & Rahman, M. O. (2024) · IEEE International Conference on Computing, Applications and Systems (COMPAS) · doi:10.1109/compas60761.2024.10796393 · Cited by 3
“YOLO models are well-known for striking a compromise between accuracy and speed, which qualifies them for real-time applications [9].”
Enhancing Fruit Recognition with YOLO v7: A Comparative Analysis Against YOLO v4
Pandey, A., Kumar, S., Verma, A., et al. (2024) · Lecture notes in networks and systems · doi:10.1007/978-3-031-70789-6_27 · Cited by 3
Patil, S., Jaybhaye, S. M., Khalifa, M. M., Kharche, S., Khatib, A., & Kshirsagar, A. (2023) · AIP conference proceedings · doi:10.1063/5.0181506 · Cited by 3
Hanafi, F. S., Dewanta, F., & Budiman, G. (2023) · International Conference on Electrical Engineering, Computer Science and Informatics (EECSI) · doi:10.1109/eecsi59885.2023.10295591 · Cited by 3
Enhancing Weakly Supervised Video Anomaly Detection with Object-Centric Features
Wang, Y.-W., Chen, Y., & Yeo, C. K. (2025) · Information · doi:10.3390/info16121042 · Cited by 3
Automated Overtake Assist System Using YOLOv4 and Mask R-CNN for Enhanced Autonomous Driving
Hegde, N. T., Rana, K., Barath, S., Nishmitha, F., & Nair, V. G. (2025) · IEEE Access · doi:10.1109/access.2025.3593023 · Cited by 3
Yermolenko, R., Klekots, D., & Gogota, O. (2024) · Naukovij žurnal «Tehnìka ta energetika» · doi:10.31548/machinery/2.2024.33 · Cited by 3
Adaptive Grid Selection Training Strategy for Tiny Object Detection
Yim, S., Cho, M., Kang, D., & Lee, S. (2025) · IEEE Access · doi:10.1109/access.2025.3613741 · Cited by 3
YOLO-DLL: an effective detection method for fabric defects
Xu, S., Wan, C., Feng, W., et al. (2025) · Textile research journal · doi:10.1177/00405175251348646 · Cited by 2
“YOLOv8 14 is currently one of the best-performing and most widely used object detection networks.”
Content-Awareness Video Compression for Roadside Surveillance Cameras
Wang, Q., Cao, X., Chen, Q., Ullah, F., & Jiang, J. (2025) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2025.3613304 · Cited by 2
“2) Loss Function Improvement: The YOLOv8 base model uses complete intersection over union (CIoU) [49] as the bounding box regression loss function.”
Digital Twin Modeling for Machine Vision Testing in Autonomous Systems
Bruzzone, A. G., Massei, M., Gotelli, M., Ferrari, R., Paoli, A. D., Giovannetti, A., & Nguyen, V. P. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-71397-2_14 · Cited by 2
Enhancing sandstorm images via color-guided spatial-frequency fusion network
Guo, Z., & Wang, B. (2025) · The Visual Computer · doi:10.1007/s00371-025-03854-4 · Cited by 2
“These issues are mainly characterized by reduced contrast, distorted color and blurred details [1], posing significant challenges for advanced computer vision taskssuchasobjectrecognition[2,3],objectdetection[4]and semantic segmentation [5].”
Crowd Anomaly Detection and Performance Evaluation using YOLO Algorithms
Kalamkar, S. P., Lodha, N. N., & Heda, L. M. (2024) · IEEE International Conference for Convergence in Technology (I2CT) · doi:10.1109/i2ct61223.2024.10543290 · Cited by 2
A Review of YOLO Object Detection Model in Forensic Evidence Analysis
Kolhatkar, P., Padule, D., & Salunkhe, P. (2023) · INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT · doi:10.55041/ijsrem26408 · Cited by 4
Turki, H., Elleuch, M., & Kherallah, M. (2024) · Communications in computer and information science · doi:10.1007/978-3-031-55729-3_5 · Cited by 2
Christoff, N., & Tonchev, K. (2024) · Electronics · doi:10.3390/electronics13132473 · Cited by 2
“Examples of object detection are You Only Look Once (YOLO) [11] or Single Shot Multi-Box Detector (SSD) [12].”
BackgroundImproved Pine Wood Nematode Disease Diagnosis System Based on Deep Learning
Xiao, J., Wu, J. C., Liu, D., Li, X., Liu, J., Su, X., & Wang, Y. (2025) · Plant Disease · doi:10.1094/pdis-06-24-1221-re · Cited by 2
Application of artificial intelligence in colonoscopy imaging for polyp analysis-A systematic review
amirmohammadi, E., Shalbaf, A., Esteki, A., sadeghi, A., moghadam, A. R., Moghtaderi, M., & Moghadam, P. K. (2026) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2026.109239 · Cited by 2
Object Detection Approaches to Identifying Hand Images with High Forensic Values
Nguyen, T. T., Wilson, C., Khan, I., & Dalins, J. (2024) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc54092.2024.10831640 · Cited by 2
“Convolutional Neural Networks (CNNs) [9] and more advanced models like faster R-CNN [10], You Only Look Once (YOLO) [11], single shot multibox detector [12], and Vision Transformers (ViT) [13] have demonstrated effectiveness in detecting various objects in an image.”
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Divya Wear- A Smart Cueing System for Visually Impaired: A Preliminary Study
Mesariya, U. R., & Joshi, K. D. (2022) · International Conference on Emerging Research in Electronics, Computer Science and Technology (ICERECT) · doi:10.1109/icerect56837.2022.10060758 · Cited by 2
Collision Avoidance System using YOLO-based Object Detection and Distance Estimation
Raj, S. A. D., Singh, A., Shekhawat, T. S., Manav Muthanna, M., & Sivasankaran, K. (2025) · International Conference on Intelligent Data Communication Technologies and Internet of Things (IDCIoT) · doi:10.1109/idciot64235.2025.10915167 · Cited by 3
“It comprises three losses: classification, localization, and confidence loss [15].”
InfluentialHyper-Progressive Single Shot Detector (HPSSD) Algorithm for Door Panel Type-B Detection
Yuan, H., Okpor, S. I., Kong, X. L., Erukainure, F. E., & Wilson, S. B. (2023) · IEEE Access · doi:10.1109/access.2023.3315873 · Cited by 2
Multiclass Classification and Defect Detection of Steel Tube Using Modified YOLO
Gurrammagari, D. R., Prabadevi, B., Gadekallu, T. R., Bhatia, S., & Saraee, M. (2023) · Communications in computer and information science · doi:10.1007/978-981-99-8184-7_32 · Cited by 2
Dai, W., Zhang, Z., Zhang, Z., et al. (2026) · Alexandria Engineering Journal · doi:10.1016/j.aej.2026.04.027 · Cited by 2
Vehicle Number Recognition Using General Surveillance Camera
Tushara, N., Sahithi, V., Haripriya, B., et al. (2023) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-981-99-1767-9_67 · Cited by 2
Classroom Activities Detection Using You Only Look Once V3
Jumadi, J., Zulfikar, W. B., Abdillah, F. F., et al. (2023) · IEEE International Conference on Computing, Engineering and Design (ICCED) · doi:10.1109/icced60214.2023.10425654 · Cited by 2
Methodology for Creating Annotated Datasets of Biological Objects in Microscopic Images
Коваленко, С. В., Kovalenko, S., Kutsenko, A., et al. (2024) · IEEE KhPI Week on Advanced Technology (KhPIWeek) · doi:10.1109/khpiweek61434.2024.10878016 · Cited by 2
“[2] (cid”
Recent AI-based smart farming for monitoring and security in sub-Saharan Africa regions
Ikegwu, A. C., Ikpo, C. V., & Akinwande, T. O. (2025) · doi:10.54646/bijiam.2025.27 · Cited by 2
Abirami, S., & Amuthaguka, D. (2025) · doi:10.1109/icoact63339.2025.11005286 · Cited by 2
“In reference [10], Comparative analysis of two stage and single stage object detectors, in various versions of YOLO.”
Privacy‐preserving labeling‐free occupancy counting sensor based on ToF camera and clustering
Jeong, J., & Park, W. (2025) · ETRI Journal · doi:10.4218/etrij.2025-0022 · Cited by 2
“In addition to privacy concerns, YOLO and other supervised learning-based methods depend on large volumes of labeled datasets for training, which are time-intensive and costly to produce [8, 9].”
Enhanced Real-Time Detector for Industrial Vision-Based Corn Impurity Detection
Zhang, X., Bian, Y., Li, X., Yu, H., Li, D., & Wu, M. (2026) · Foods · doi:10.3390/foods15061065 · Cited by 3
“Simultaneously, the existing large models feature substantial parameter counts and high computational complexity, making deployment challenging on computationally constrained industrial embedded edge devices and limiting their real-time application on production lines [23].”
Object Classification For Autonomous Vehicles using a One-stage Detector Algorithm
Maganti, J., Rajeswara Rao, D., & Amballa, S. (2024) · IEEE World Conference on Applied Intelligence and Computing (AIC) · doi:10.1109/aic61668.2024.10730993 · Cited by 2
“Fig 1 [3] illustrates the YOLO algorithm’s pipeline, which processes 448 x 448 RGB images using a single end-to-end convolutional neural network.”
MethodKutyrev, A. I., Khort, D. O., Smirnov, I. G., et al. (2025) · E3S Web of Conferences · doi:10.1051/e3sconf/202561403008 · Cited by 2
Eynet: extended YOLO for airport detection in remote sensing images
Mirhajianmoghadam, H., & Haghighi, B. B. (2025) · Pattern Analysis and Applications · doi:10.1007/s10044-025-01595-7 · Cited by 2
Optical and Magnetometric Data Integration for Landmine Detection with UAV
Stankevich, S. A., & Saprykin, I. Y. (2024) · WSEAS TRANSACTIONS ON ENVIRONMENT AND DEVELOPMENT · doi:10.37394/232015.2024.20.96 · Cited by 2
“…[13] architecture, especially its version Ultralytics YOLOv8 [14], is used for landmine detection in visible imagery (Figure 1), [15], [16], [17], [18], [19], while statistical anomaly extraction within the circular scanning window is utilized for landmine detection in thermal imagery (Figure 2).”
Chandra, N. S., Vaidya, H., Sawant, S. T., Gite, S., & Pradhan, B. (2025) · Computer Modeling in Engineering & Sciences · doi:10.32604/cmes.2025.064395 · Cited by 2
Computational intelligence in rock engineering management systems
Lenji, A. R., Mehmandari, T. A., Ranjbar, S., Tee, K. F., & Fahimifar, A. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-23663-1.00001-x · Cited by 3
Miglionico, G. C., Di Rienzo, F., Ducange, P., Marcelloni, F., & Vallati, C. (2025) · 2025 International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn64981.2025.11227764 · Cited by 2
“Subsequently, the advent of single-stage models [5], such as YOLO (You Only Look Once) [6], revolutionized the field by significantly enhancing speed and further accuracy.”
Liu, S., Ge, J., Zhang, L., et al. (2025) · Building and Environment · doi:10.1016/j.buildenv.2025.114091 · Cited by 2
Enhancing Surveillance and Face Recognition with YOLO-Based Object Detection
Patil, S., Meshram, D., Bohra, M., et al. (2023) · Smart innovation, systems and technologies · doi:10.1007/978-981-99-3982-4_32 · Cited by 2
Detection of Tiny Objects for Maritime Search and Rescue Operations
Kapoor, S., Kumar, M., Kaushal, M., et al. (2024) · Algorithms for intelligent systems · doi:10.1007/978-981-97-0180-3_26 · Cited by 2
A Lightweight Tea Bud-Grading Detection Model for Embedded Applications
Tang, L., Yang, Y., Fan, C., & Pang, T. (2025) · Agronomy · doi:10.3390/agronomy15030582 · Cited by 3
UAV Object Detection Based on Joint YOLO and Transformer
Gao, Y., Ding, R., Zhou, F., et al. (2024) · International Conference on Ubiquitous Communication (Ucom) · doi:10.1109/ucom62433.2024.10695939 · Cited by 2
“Among them, the You Only Look Once (YOLO) series of models [11] are popular for its real-time reasoning capabilities, high accuracy and extensive attention.”
BackgroundAutomated detection and Threshold-Based alerting of stray dogs
Hamadani, A., Hamadani, H., Boje, P. H., Gani, A. O. B., & Abdullah, Q. (2025) · Urban Ecosystems · doi:10.1007/s11252-025-01815-2 · Cited by 2
“In consonance with our results, research over the years indicates that YOLO outperforms many object detection algorithms for identification and segmentation of objects (Diwan et al. 2023).”
Visual Intelligence: Machine Learning Approaches to Image Filtering and Identification
Khammar, M. M. (2024) · INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT · doi:10.55041/ijsrem29940 · Cited by 2
Hawashin, D., Salah, K., Jayaraman, R., & Yaqoob, I. (2025) · Technology in Society · doi:10.1016/j.techsoc.2025.102959 · Cited by 3
Ratanakul, N., Urumporn, T., Sultan, N., et al. (2025) · International Joint Conference on Computer Science and Software Engineering · doi:10.1109/jcsse67377.2025.11297913 · Cited by 2
“YOLO [6] and Faster R-CNN [7] are popular frameworks that have shown high accuracy across diverse datasets.”
Segregation of Dehusked Arecanut using Artificial Intelligence Technique on Raspberry Pi
Adesh, N. D., Harshith, M., Bhardwaj, S., et al. (2023) · IEEE North Karnataka Subsection Flagship International Conference (NKCon) · doi:10.1109/nkcon59507.2023.10395973 · Cited by 2
YOLOv8n-BWG-enhanced drone smoke detection: Advancing environmental monitoring efficiency
Zhang, Y., & Wu, G. (2025) · PLoS ONE · doi:10.1371/journal.pone.0322448 · Cited by 2
“These algorithms are further categorized into single-stage (e.g., SSD [9], YOLO series [10]) and two-stage (e.g., R-CNN [11], Fast R-CNN [12], Faster R-CNN [13]) approaches.”
Detection and recognition of aircraft vehicle-A supple approach using deep pliable YOLOv5
Panda, B. S., Gopal, K. M., Satpathy, R., & Panda, G. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-024-19597-8 · Cited by 2
Ling, J., Meng, H., & Gong, D. (2025) · Applied Sciences · doi:10.3390/app15169207 · Cited by 2
Comparison of Deep Learning and Yolov8 Models for Fox Detection Around the Henhouse
Çimen, M. E. (2024) · Journal of Smart Systems Research · doi:10.58769/joinssr.1498561 · Cited by 3
Real-time identification of traffic actors using YOLOv7
Polagani, P. K., Siddi, L. P., & Pujitha, M. V. (2024) · CRC Press eBooks · doi:10.1201/9781003471059-23 · Cited by 2
Detection of Motorcycle Headlights Using YOLOv5 and HSV
Oktavia, V. R., Johan, A. W. S. B., Ananta, W. G., et al. (2023) · Teknika · doi:10.34148/teknika.v12i3.682 · Cited by 2
A Recyclable Waste Image Recognition System with YOLOv8 for Children's Environmental Education
Zambri, A. F., Abdul-Rahman, S., Sabri, N. M., et al. (2024) · Engineering Technology & Applied Science Research · doi:10.48084/etasr.7879 · Cited by 2
“The study in [6] explored the potential to incorporate image recognition into educational tools to enhance children's understanding of waste management processes.”
BackgroundPENDETEKSIAN OBJEK MENGGUNAKAN OPENCV DAN METODE YOLOv4-TINY UNTUK MEMBANTU TUNANETRA
Yusup, R. M., Anugrah, A. F., Muslimah, D. D., et al. (2024) · Journal of Computer Science and Information Technology · doi:10.59407/jcsit.v1i2.532 · Cited by 2
Rao, K. M., Agrawal, D., Reyya, S., & Varma, P. (2024) · Lecture notes in electrical engineering · doi:10.1007/978-981-97-6714-4_6 · Cited by 2
Likitvisetpong, M., Erjongmanee, S., Suwanagood, E., et al. (2024) · International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON) · doi:10.1109/ecti-con60892.2024.10594824 · Cited by 2
Wang, Y., Zhang, B., Qu, A., & Gao, Y. (2026) · Information Processing in Agriculture · doi:10.1016/j.inpa.2026.03.013 · Cited by 2
TCone-YOLO: enhanced long-range and low-parameter traffic cone detection for autonomous racing cars
Huang, W., Liao, Z., Wei, Z., Lu, Y., & Li, H. (2025) · Signal Image and Video Processing · doi:10.1007/s11760-025-04565-y · Cited by 2
“The anchor number adapts to the distribution of the aspect ratio of the object size in the dataset [22], further affecting the detection performance by affecting theclusteringqualityof theanchor shapes.”
Ahmadsaidulu, S., Tiwari, A., Neelapu, B. C., Jain, P., & Banoth, E. (2024) · International Journal of Imaging Systems and Technology · doi:10.1002/ima.23204 · Cited by 2
An artificial intelligence-enabled consumables tracking system for medical laboratories
Sritart, H., Tosranon, P., & Taertulakarn, S. (2024) · Journal of Intelligent Systems · doi:10.1515/jisys-2023-0208 · Cited by 2
Recyclable Waste Image Classification using Convolutional Neural Networks
Fahad, A. B., Alam, S. E., Farjana, M., et al. (2023) · International Conference on Computer and Information Technology (ICCIT) · doi:10.1109/iccit60459.2023.10441091 · Cited by 2
Drones and Deep Learning for Detecting Fish Carcasses During Fish Kills
Fernández-Figueroa, E. G., Rogers, S. R., & Neupane, D. (2025) · Drones · doi:10.3390/drones9070482 · Cited by 2
Gitau, S. N., El-Malek, A. H. A., Sayed, M. S., et al. (2024) · doi:10.1109/ict62760.2024.10606122 · Cited by 2
“However, some models like You Only Look Once (YOLO) are more appropriate for detection and segmentation, although they could still be applied in classification tasks [29].”
MethodYuan, Z., Hao, C., Wang, Y., et al. (2026) · Light Advanced Manufacturing · doi:10.37188/lam.2026.064 · Cited by 2
Using Artificial Intelligence Deep Learning to Detect and Prevent Retained Surgical Sponges
Hmood, Z., Ghayda, R. A., Santos, A. P., & Stuart, D. B. (2025) · Journal of Patient Safety · doi:10.1097/pts.0000000000001411 · Cited by 2
Efficient and Precise Detection of Surface Defects on PCBs: A YOLO Based Approach
Pan, L., Wang, W., Lu, K., Zhang, J., Chen, P., Ni, J., Zhu, C., & Wang, B. (2023) · Lecture notes in computer science · doi:10.1007/978-981-99-4742-3_50 · Cited by 3
Shao, X., Shen, Y., Sun, P., et al. (2025) · World Journal of Surgical Oncology · doi:10.1186/s12957-025-04087-4 · Cited by 2
“This study employed YOLOv11, the latest in the YOLO series, to develop an AI-based surgical navigation model featuring enhanced performance in object detection, segmentation, and pose estimation tasks due to its architectural innovations [24, 25].”
Detection of Hyperbolic Reflections on Real GPR Data Using YOLO Model Trained with Synthetic Data
Bugarinović, Ž., Gavrilović, M., Ristić, A., et al. (2024) · International Conference on Telecommunications and Signal Processing (TSP) · doi:10.1109/tsp63128.2024.10605917 · Cited by 2
“The YOLO algorithm has a clear structure, which enables its application in various scenarios [26], [27], and in a large number of analyzes [28] it proved to be one of the most successful algorithms for the detection of various complex objects.”
BackgroundRío-Álvarez, Á., Marcos, P. G., González, P. P., Serrano‐Pertierra, E., Novelli, A., Fernández‐Sánchez, M. T., & González, V. M. (2024) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-024-03202-z · Cited by 2
A Convolutional Neural Network as a Potential Tool for Camouflage AssessmentPreprint
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PDD-AGENT: Multimodal Large Language Model-Driven AI Agent for Enhanced Plant Disease Diagnosis
Qin, L., Wu, X., Dong, X., et al. (2025) · Proceedings - International Conference on Image Processing · doi:10.1109/icip55913.2025.11084359 · Cited by 2
MC-YOLOv5: A small target detection model based on YOLOv5
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Moving Object Detection Using Deep Learning AlgorithmsPreprint
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Li, W., Zhu, W., Wang, Q., Gao, F., Han, K., & Jin, X. (2026) · Agronomy · doi:10.3390/agronomy16090907 · Cited by 2
Synthetic soils for ecological and synthetic biology applications
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Object Detection for Retail Product Recognition
Pannoy, N., Nonsiri, S., & Makdee, S. (2024) · International Conference on Business and Industrial Research (ICBIR) · doi:10.1109/icbir61386.2024.10875711 · Cited by 2
Real-time theft detection in urban surveillance: A comparative analysis of YOLO-based approach
Solak, S. B., Cetin, B., & Uçar, M. H. B. (2025) · Journal of Engineering Research · doi:10.1016/j.jer.2025.11.009 · Cited by 3
The Impact of REER Volatility on Economic Activity in Ukraine
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Lu, Z., Wang, J., Zhu, J., et al. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.129811 · Cited by 2
Metadata
- Authors
- Diwan, T., Anirudh, G., & Tembhurne, J. V.
- Affiliations
- Indian Institute of Information Technology, Nagpur (IN)
- Central University of Rajasthan (IN)
- Corresponding author
- Diwan, Tausif
- Venue
- Multimedia Tools and Applications
- Published
- 2022-08-08
- Volume
- 82
- Issue
- 6
- Pages
- 9243-9275
- ISSN
- 1380-7501 (ISSN-L)
- 1573-7721
- PMC
- PMC9358372
- OpenAlex
- W4291819654
- Cited by
- 1,549
- References
- 73
- Citations by year
- 2026: 308 · 2025: 542 · 2024: 468 · 2023: 218 · 2022: 13
- FWCI
- 101.55
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Multimedia Tools and Applications (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Visual Attention and Saliency Detection
- Keywords
- object detection, YOLO, Fast R-CNN, inference latency, detection accuracy, real-time object detection
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