Cited by 2,783Advanced Neural Network ApplicationsDomain Adaptation and Few-Shot LearningAdvanced Image and Video Retrieval Techniques

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A Review of Yolo Algorithm Developments

Open access

Jiang, Peiyuan, Ergu, Daji, Liu, Fangyao, Cai, Ying, Ma, Bo·2022·Procedia Computer Science

DOI 10.1016/j.procs.2022.01.135

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Abstract

Object detection techniques are the foundation for the artificial intelligence field. This research paper gives a brief overview of the You Only Look Once (YOLO) algorithm and its subsequent advanced versions. Through the analysis, we reach many remarks and insightful results. The results show the differences and similarities among the YOLO versions and between YOLO and Convolutional Neural Networks (CNNs). The central insight is the YOLO algorithm improvement is still ongoing.This article briefly describes the development process of the YOLO algorithm, summarizes the methods of target recognition and feature selection, and provides literature support for the targeted picture news and feature extraction in the financial and other fields. Besides, this paper contributes a lot to YOLO and other object detection literature.

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You Only Look Once: Unified, Real-Time Object Detection
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YOLOv4: Optimal Speed and Accuracy of Object Detection
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Apple detection during different growth stages in orchards using the improved YOLO-V3 model
Tian, Y., Yang, G., Wang, Z., Wang, H., Li, E., & Liang, Z. (2019) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2019.01.012 · Cited by 1,043
A Robust Real-Time Automatic License Plate Recognition Based on the YOLO Detector
Laroca, R., Severo, E. B., Junior, L. A. Z., Oliveira, L. S., Gonçalves, G. R., Schwartz, W. R., & Menotti, D. (2018) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2018.8489629 · Cited by 670
YOLO-LITE: A Real-Time Object Detection Algorithm Optimized for Non-GPU Computers
Huang, R., Pedoeem, J., & Chen, C. (2018) · 2018 IEEE International Conference on Big Data (Big Data) · doi:10.1109/bigdata.2018.8621865 · Cited by 622
A Review of Object Detection Models Based on Convolutional Neural Network
Sultana, F., Sufian, A., & Dutta, P. (2020) · Advances in intelligent systems and computing · doi:10.1007/978-981-15-4288-6_1 · Cited by 327
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Wang, Z., & Liu, J. (2017) · Chinese Control Conference (CCC) · doi:10.23919/chicc.2017.8029130 · Cited by 268
A YOLO-Based Traffic Counting System
Lin, J., & Sun, M.-T. (2018) · Conference on Technologies and Applications of Artificial Intelligence (TAAI) · doi:10.1109/taai.2018.00027 · Cited by 116
A Vehicle Detection Method for Aerial Image Based on YOLO
Lu, J., Ma, C., Li, L., Xing, X., Zhang, Y., Wang, Z., & Xu, J. (2018) · Journal of Computer and Communications · doi:10.4236/jcc.2018.611009 · Cited by 101

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IR-YOLO: Real-Time Infrared Vehicle and Pedestrian Detection
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“To tackle the previously mentioned concerns opts for YOLOv5s [14] as the basic model and improves upon it to boost recognition accuracy while reducing computational overhead.”
Micro LED defect detection with self-attention mechanism-based neural network
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HeCapsNet : An enhanced capsule network for automated heel disease diagnosis using lateral foot X‐Ray images
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Research on Automated Fiber Placement Surface Defect Detection Based on Improved YOLOv7
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Bana, G., Lamadie, F., Charton, S., Randriamanantena, T., Lucor, D., & Sheibat‐Othman, N. (2024) · Frontiers in Chemical Engineering · doi:10.3389/fceng.2024.1415453 · Cited by 11
A Method for Real-Time Lung Nodule Instance Segmentation Using Deep Learning
Santone, A., Mercaldo, F., & Brunese, L. (2024) · Life · doi:10.3390/life14091192 · Cited by 11
“The YOLO architecture consists of three main components: the backbone, the neck, and the head [54–59]: 1.”
Background
Performance Evaluation of YOLOv5 and YOLOv8 Object Detection Algorithms on Resource-Constrained Embedded Hardware Platforms for Real-Time Applications
Peserico, G., & Morato, A. (2024) · IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) · doi:10.1109/etfa61755.2024.10710707 · Cited by 11
Enhanced YOLOv5 for Traffic Object Detection: Combined Channel Attention and Spatial Attention Mechanism
Yan, X., & Zhu, Y. (2024) · Asia Symposium on Image Processing (ASIP) · doi:10.1109/asip63198.2024.00013 · Cited by 12
Vision-based identification of tire inflation pressure using Tire-YOLO and deflection
Zhang, J., Peng, J., Kong, X., Deng, L., & OBrien, E. J. (2024) · Measurement · doi:10.1016/j.measurement.2024.116228 · Cited by 11
Lightweight prohibited items detection model in X-ray images based on improved YOLOv7-tiny
Zhang, H., Teng, W., He, X., Que, H., & Zhang, Y. (2024) · Journal of the Franklin Institute · doi:10.1016/j.jfranklin.2024.107421 · Cited by 11
Modulating emotional states of rats through a rat-like robot with learned interaction patterns
Jia, G., Chen, Z., Zhang, Y., Bing, Z., Quan, Z., Chen, X., Knoll, A., Huang, Q., & Shi, Q. (2024) · Nature Machine Intelligence · doi:10.1038/s42256-024-00939-y · Cited by 11
Diagnosis of approximal caries in children with convolutional neural networks based detection algorithms on radiographs: A pilot study
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“Consistent with established practices [14, 15], the dataset was partitioned into 80% training data and 20% test data.”
Study on efficient recognition and accurate localization method of waste plastic bottles based on deep learning
Xie, S., Wu, H., Mao, W., Chu, X., Meng, Y., & Yang, X. (2025) · Ecological Informatics · doi:10.1016/j.ecoinf.2025.103020 · Cited by 13
YOLOV8-MR: An Improved Lightweight YOLOv8 Algorithm for Tomato Fruit Detection
Li, X., Cai, C., Yang, Y., & Song, B. (2025) · IEEE Access · doi:10.1109/access.2025.3533489 · Cited by 11
“Furthermore, first-stage algorithms, represented by SSD [19] and YOLO [20], offer faster detection speeds and comparable accuracy to two-stage algorithms, along with improved portability and real-time performance.”
Surface defect detection of planar optical components based on OPT-YOLO
Huang, J., Zhang, W., Jin, W., & Hu, H. (2025) · Optics and Lasers in Engineering · doi:10.1016/j.optlaseng.2025.108974 · Cited by 12
Synchronous detection of internal and external defects of citrus by structured-illumination reflectance imaging coupling with improved YOLO v7
Cai, Z., Zhang, Y., Li, J., Zhang, J., & Li, X. (2025) · Postharvest Biology and Technology · doi:10.1016/j.postharvbio.2025.113576 · Cited by 11
The combined effect of decreased stomatal density and aperture increases water use efficiency in maize
Barl, L., Benato, B. D., Genze, N., Grimm, D. G., Gigl, M., Dawid, C., Schön, C., & Avramova, V. (2025) · Scientific Reports · doi:10.1038/s41598-025-94833-1 · Cited by 13
“It is a well-established field of machine learning, with models ranging from one-stage object detectors (Single-Shot-Detectors 74 or the You-Only-Look-Once 75,76 family) to two-stage object detectors (R-CNN 77,78 family).”
LBA-YOLO: A novel lightweight approach for detecting micro-cracks in building structures
Ren, W., & Zhong, Z. (2025) · PLoS ONE · doi:10.1371/journal.pone.0321640 · Cited by 11
“In contrast, one-stage detection models (e.g., SSD and YOLO) merge classification and localisation into a single step with higher real-time performance and lower computational complexity [27,28].”
Computer Vision-Based Safety Monitoring of Mobile Scaffolding Integrating Depth Sensors
Abbas, M. S., Hussain, R., Zaidi, S. F. A., Lee, D., & Park, C. (2025) · Buildings · doi:10.3390/buildings15132147 · Cited by 11
DFAS-YOLO: Dual Feature-Aware Sampling for Small-Object Detection in Remote Sensing Images
Liu, X., Zhou, S., Ma, J., Sun, Y., Zhang, J., & Zuo, H. (2025) · Remote Sensing · doi:10.3390/rs17203476 · Cited by 14
Automatic Roadside Feature Detection Based on Lidar Road Cross Section Images
Brkić, I., Miler, M., Ševrović, M., & Medak, D. (2022) · Sensors · doi:10.3390/s22155510 · Cited by 12
“I recent years, five v rsions of YOLO have been released, each time with signifi an changes in the algorit m structure that improved both the inference time and the accuracy of the algorithm [36].”
Background
Study on wear debris characteristics of different polyethylene under reciprocating sliding and compound motion modes
Li, J., Wang, S., Fu, S., & Peng, Y. (2023) · Wear · doi:10.1016/j.wear.2023.205062 · Cited by 11
Testing and Evaluation of Low-Cost Sensors for Developing Open Smart Campus Systems Based on IoT
Neis, P., Warch, D., & Hoppe, M. (2023) · Sensors · doi:10.3390/s23208652 · Cited by 12
“For object detection to determine trafﬁc, the YOLO algorithm, which is available in various versions, is also often used [63].”
Method
Investigation of YOLO models in the detection and classification of multiple negative road anomalies
Kulkarni, S., Mittal, N., Gupta, R. R., & Ponnlagu, R. N. (2023) · International Conference on Computing, Communication and Networking Technologies (ICCCNT) · doi:10.1109/icccnt56998.2023.10306347 · Cited by 11
“In [27], different versions of YOLO and their features are discussed.”
Background
FLARE: Defending Federated Learning Against Model Poisoning Attacks via Latent Space Representations
Wang, N., Zhang, C., Xiao, Y., Chen, Y., Lou, W., & Hou, Y. T. (2024) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2024.3521297 · Cited by 12
“Compared with the image classiﬁcation tasks, the training dataset image needs to specify the class of each object and the location information of every object in the whole image [42], [43].”
Artificial Intelligence Methods for Identifying and Localizing Abnormal Parathyroid Glands: A Review Study
Apostolopoulos, I. D., Papandrianos, N. I., Papageorgiou, E. I., & Apostolopoulos, D. J. (2022) · Machine Learning and Knowledge Extraction · doi:10.3390/make4040040 · Cited by 12
“Under this setup, the authors employed the YOLO v5 [35] network, a real-time object detection DL model, to identify and localize PGs.”
Method
Scene Text detection and Recognition by Using Multi-Level Features Extractions Based on You Only Once Version Five (YOLOv5) and Maximally Stable Extremal Regions (MSERs) with Optical Character Recognition (OCR)
Almousawee, E., & Abbadi, N. K. E. (2022) · Al-Salam Journal for Engineering and Technology · doi:10.55145/ajest.2023.01.01.002 · Cited by 11
Development of an Artificial Intelligence (AI) Based Visual Counting System for the Food Industry
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“The original YOLO algorithm [8] was published in 2015, and it was designed to be fast and efficient while maintaining good accuracy.”
Method
Real-time analysis of soccer ball–player interactions using graph convolutional networks for enhanced game insights
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Coarse-to-fine visual autonomous unmanned aerial vehicle landing on a moving platform
Cui, Q., Liu, M., Huang, X., & Gao, M. (2023) · Biomimetic Intelligence and Robotics · doi:10.1016/j.birob.2023.100088 · Cited by 10
Moving Object Detection Based on Enhanced Yolo-V2 Model
Safaldin, M., Zaghden, N., & Mejdoub, M. (2023) · International Congress on Human-Computer Interaction, Optimization and Robotic Applications (ICHORA) · doi:10.1109/hora58378.2023.10156680 · Cited by 10
Enhanced YOLOv4 for Facilitating Public Safety Management amidst Protests and Riots
Thiruthanigesan, K., Nawarathna, R. D., & Ragel, R. (2023) · IEEE International Conference on Industrial and Information Systems (ICIIS) · doi:10.1109/iciis58898.2023.10253556 · Cited by 10
“proposed an accelerated YOLOv4 algorithm that employed a sparsity-aware training strategy and a low-rank approximation method, achieving a higher speedup and comparable accuracy than the original YOLOv4 algorithm [17]–[20].”
Method
System of Counting Green Oranges Directly from Trees Using Artificial Intelligence
Gremes, M. F., Fermo, I. R., Krummenauer, R., Flores, F. C., Andrade, C. M. G., & Lima, O. C. D. M. (2023) · AgriEngineering · doi:10.3390/agriengineering5040111 · Cited by 13
UTD-YOLO: underwater trash detection model based on improved YOLOv5
Wu, G., Ge, Y., & Yang, Q. (2023) · Journal of Electronic Imaging · doi:10.1117/1.jei.32.6.063034 · Cited by 11
“23,24 This paper proposes a UTD-YOLO model.”
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Ontology based autonomous robot task processing framework
Ge, Y., Zhang, S., Cai, Y., Lu, T., Wang, H., Hui, X., & Wang, S. (2024) · Frontiers in Neurorobotics · doi:10.3389/fnbot.2024.1401075 · Cited by 10
Self-citation
Anemia detection and classification from blood samples using data analysis and deep learning*
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“These limitations are resolved by using the YOLOv6-based classification method [43], which is proposed in this paper.”
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“Jiang et al. [30] find that the YOLO (You Only Look Once) family of deep architectures is one of the best detection techniques to date.”
Optimized YOLOv8 based on SGW for surface defect detection of silicon wafer
Zhu, G., Peng, J., Sheng, L., Chen, T., He, Z., & Lu, X. (2024) · Physica Scripta · doi:10.1088/1402-4896/ad8cad · Cited by 10
YOLOv8-GO: A Lightweight Model for Prompt Detection of Foliar Maize Diseases
Jiang, T., Du, X., Zhang, N., Sun, X., Li, X., Tian, S., & Liang, Q. (2024) · Applied Sciences · doi:10.3390/app142110004 · Cited by 12
YOLOv5DA: An Improved YOLOv5 Model for Posture Detection of Grouped Pigs
Shi, W., Wang, X., Li, X., Fu, Y., Liu, X., & Wang, H. (2024) · Applied Sciences · doi:10.3390/app142210104 · Cited by 10
AAMS-YOLO: enhanced farmland parcel detection for high-resolution remote sensing images
Wang, B., Zhou, Y., Zhu, W., Feng, L., He, J., Wu, T., Luo, J., & Zhang, X. (2024) · International Journal of Digital Earth · doi:10.1080/17538947.2024.2432532 · Cited by 10
“One-stage detectors, such as YOLO (You Only Look Once) and SSD (Single Shot MultiBox Detector), utilize a single network to perform localization and classification simultaneously (Liu et al. 2016; Jiang et al. 2022).”
Automated Visual Inspection for Precise Defect Detection and Classification in CBN Inserts
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Enhancing YOLOv5 Performance for Small-Scale Corrosion Detection in Coastal Environments Using IoU-Based Loss Functions
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Efficient detection of eyes on potato tubers using deep-learning for robotic high-throughput sampling
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“Each iteration introduces architectural improvements to tackle speci ﬁ c challenges in object detection (Jiang et al., 2022; Terven et al., 2023; Morbekar et al., 2020).”
Deep-learning image processing of interferometric particle imaging in icing wind tunnel
Wang, X., Wang, Z., Wang, B., Zhang, H., Wu, Y., Li, S., Xia, Q., Zhang, Y., Feng, F., & Wu, X. (2024) · Measurement · doi:10.1016/j.measurement.2024.116324 · Cited by 10
Data generation using Pix2Pix to improve YOLO v8 performance in UAV-based Yuzu detection
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An Improved YOLOv9s Algorithm for Underwater Object Detection
Zhou, S., Wang, L., Chen, Z., Zheng, H., Lin, Z., & Li, H. (2025) · Journal of Marine Science and Engineering · doi:10.3390/jmse13020230 · Cited by 10
Deep Learning Models for UAV-Assisted Bridge Inspection: A YOLO Benchmark Analysis
Phan, T.-N., Nguyen, H. H., Ha, T.-T.-H., Thai, H.-T., & Le, K.-H. (2024) · International Conference on Advanced Technologies for Communications · doi:10.1109/atc63255.2024.10908331 · Cited by 10
“YOLOv5 built upon many of the improvements made in YOLOv4, including flexible control of model size, application of Hardswish activation function, and data enhancement [16], but the major difference is that it was built based on PyTorch framework instead of DarkNet.”
Real-Time Data-Driven Method for Bolt Defect Detection and Size Measurement in Industrial Production
Yang, J., & Lee, C.-H. (2025) · Actuators · doi:10.3390/act14040185 · Cited by 12
“The achievement of these tasks typically relies on deep learning models, such as ResNet [9] for image classification, YOLO (You Only Look Once) [10,11] for object detection, and Mask R-CNN [12] for object segmentation.”
CN2VF-Net: A Hybrid Convolutional Neural Network and Vision Transformer Framework for Multi-Scale Fire Detection in Complex Environments
Ahmad, N., Akbar, M., Alkhammash, E. H., & Jamjoom, M. M. (2025) · Fire · doi:10.3390/fire8060211 · Cited by 10
“For instance, YOLOv11 has addressed some of the shortcomings in earlier versions, such as low-contrast detection and high false positives, and therefore is now a first-line option for real-time fire detection [11,12].”
Mathematics teacher educator noticing: developing computer-assisted, ecological investigation of critical environmental cues
Jazby, D., Ochôa, X., Chan, M. C. E., & DRIEL, J. H. V. (2025) · Journal of Mathematics Teacher Education · doi:10.1007/s10857-025-09700-7 · Cited by 10
“The cards were colour coded so that the position of each card could be tracked by deploying a simple object detection algorithm (YOLO model, Jiang et al., 2022) that tracked colours to the fixed camera footage post-lesson (see Fig.”
Experimental Validation of UAV Search and Detection System in Real Wilderness Environment
Dumenčić, S., Lanča, L., Jakac, K., & Ivić, S. (2025) · Drones · doi:10.3390/drones9070473 · Cited by 12
DeepSIGNAL-ITS—Deep Learning Signal Intelligence for Adaptive Traffic Signal Control in Intelligent Transportation Systems
Medvei, M. M., Bordei, A.-V., Niță, Ș. L., & Ţăpuş, N. (2025) · Applied Sciences · doi:10.3390/app15179396 · Cited by 11
Combining ultralow-altitude drone phenotyping with deep learning analytics to assess resistance and disease dynamics of Fusarium head blight in wheat
Liu, S., Dai, J., Huang, J., Wen, Z., Zhang, W., Shen, L., Jackson, R. J., Wang, X., Deakin, G., Xiao, J., et al. (2025) · The Crop Journal · doi:10.1016/j.cj.2025.08.009 · Cited by 10
License plate recognition system for complex scenarios based on improved YOLOv5s and LPRNet
Wang, X., Wang, M., Guo, H., Li, J., Xian, W., & Zhang, Y. (2025) · Scientific Reports · doi:10.1038/s41598-025-18311-4 · Cited by 10
Application of YOLO Algorithm for Intelligent Transportation Systems: A Survey and New Perspectives
Lv, X., Chen, T., Song, C., Yang, C., & Ping, T. (2025) · International Journal of Distributed Sensor Networks · doi:10.1155/dsn/2859040 · Cited by 15
“Variations among di ﬀ erent YOLO versions have resulted in substantial enhancements in both accuracy and speed [54, 55].”
Image-based bolt self-localization and bolt-loosening detection using deep learning and an improved homography-based prospective rectification method
Xie, C., Luo, J., Tang, K., & Zhong, Y. (2023) · Advances in Structural Engineering · doi:10.1177/13694332231157260 · Cited by 10
“In the ﬁ eld of target detection algorithms, the YOLO algorithm is favored by scholars due to its small model size, fast computational speed and strong generalization ability (Jiang et al., 2022).”
Method
A fine-tuned YOLOv5 deep learning approach for real-time house number detection
Taşyürek, M., & Öztürk, C. (2023) · PeerJ Computer Science · doi:10.7717/peerj-cs.1453 · Cited by 12
Performance Comparison of Object Detection Networks for Shrapnel Identification in Ultrasound Images
Torres, S. I. H., Hennessey, R., & Snider, E. J. (2023) · Bioengineering · doi:10.3390/bioengineering10070807 · Cited by 10
“The YOLO (You Only Look Once) series of object detection models use deep convolutional neural networks to learn features that are then used to identify objects in images [25].”
Background
Few-Shot Object Detection in Remote Sensing Imagery via Fuse Context Dependencies and Global Features
Wang, B., Ma, G., Sui, H., Zhang, Y., Zhang, H., & Zhou, Y. (2023) · Remote Sensing · doi:10.3390/rs15143462 · Cited by 10
A Machine Learning Approach for Automated Detection of Critical PCB Flaws in Optical Sensing Systems
Chen, P., & Xie, F. (2023) · Photonics · doi:10.3390/photonics10090984 · Cited by 10
Determination of the Rate Dependence of Damage Formation in Metallic‐Intermetallic Mg–Al–Ca Composites at Elevated Temperature using Panoramic Image Analysis
Medghalchi, S., Zubair, M., Karimi, E., Sandlöbes-Haut, S., Kerzel, U., & Korte‐Kerzel, S. (2023) · Advanced Engineering Materials · doi:10.1002/adem.202300956 · Cited by 10
Human Object Detection for Real-Time Camera using Mobilenet-SSD
Muwardi, R., Permana, J. M. R., Gao, H., & Yunita, M. (2023) · Journal of Integrated and Advanced Engineering (JIAE) · doi:10.51662/jiae.v3i2.108 · Cited by 10
“There are various object detection algorithms, such as YOLO, R-CNN, Mask R-CNN, MobileNet, and SqueezeDet [8, 9, 10, 11, 12].”
Method
Leveraging Bounding Box Annotations for Fish Segmentation in Underwater Images
Sánchez, J. S., Lisani, J.-L., Catalán, I. A., & Álvarez-Ellacuría, A. (2023) · IEEE Access · doi:10.1109/access.2023.3330968 · Cited by 11
“Popular object detection architectures such as Faster R-CNN [17] and YOLO, in its different versions [18], [19], [20], have been used for fish detection and abundance estimation [21], [22], [23], [24], [25], [26].”
Method
Addressing the Gaps of IoU Loss in 3D Object Detection with IIoU
Ravi, N., & El‐Sharkawy, M. (2023) · Future Internet · doi:10.3390/fi15120399 · Cited by 10
Yolo Versions Architecture: Review
Hasan, R. H., Hassoon, R. M., & Aboud, I. S. (2023) · International Journal of Advances in Scientific Research and Engineering · doi:10.31695/ijasre.2023.9.11.7 · Cited by 14
An efficient detection model based on improved YOLOv5s for abnormal surface features of fish
Zhang, Z., Lu, X., & Cao, S. (2024) · Mathematical Biosciences & Engineering · doi:10.3934/mbe.2024076 · Cited by 10
“The difference between the five models is the depth and width of the network [24].”
Background
An annotated satellite imagery dataset for automated river barrier object detection
Wu, J., Li, W.-J., Du, H.-B., Wan, Y., Yang, S.-F., & Xiao, Y. (2025) · Scientific Data · doi:10.1038/s41597-025-04590-z · Cited by 10
Multi-Dimensional Information Fusion You Only Look Once Network for Suspicious Object Detection in Millimeter Wave Images
Chen, Z., Tian, R., Xiong, D., Yuan, C., Tang, L., & Shi, Y. (2024) · Electronics · doi:10.3390/electronics13040773 · Cited by 9
Research on Vision-Based Servoing and Trajectory Prediction Strategy for Capturing Illegal Drones
Ma, J., Chen, P., Xiong, X., Liangcheng, Z., Yu, S., & Zhang, D. (2024) · Drones · doi:10.3390/drones8040127 · Cited by 11
An Overview of Moving Object Detection Using YOLO Deep Learning Models
Dwivedi, U., Joshi, K., Shukla, S. K., & Rajawat, A. S. (2024) · International Conference on Disruptive Technologies (ICDT) · doi:10.1109/icdt61202.2024.10489800 · Cited by 9
“In this paper, we presented an outline of real-time object identification methods based on the YOLO deep learning model [1-5][8-9].”
BackgroundMethodInfluential
YOLO-ET: A Machine Learning model for detecting, localising and classifying anthropogenic contaminants and extraterrestrial microparticles optimised for mobile processing systems
Pinault, L. J., Yano, H., Okudaira, K., & Crawford, I. (2024) · Astronomy and Computing · doi:10.1016/j.ascom.2024.100828 · Cited by 9
An Improved YOLO Network for Insulator and Insulator Defect Detection in UAV Images
Zhou, F., Liu, L., Hu, H., Jin, W., Zheng, Z., Li, Z., Ma, Y., & Wang, Q. (2024) · Photogrammetric Engineering & Remote Sensing · doi:10.14358/pers.23-00074r2 · Cited by 9
A Wearable Visually Impaired Assistive System Based on Semantic Vision SLAM for Grasping Operation
Fei, F., Xian, S., Yang, R., Wu, C., & Lu, X. (2024) · Sensors · doi:10.3390/s24113593 · Cited by 9
Complex Scene Occluded Object Detection with Fusion of Mixed Local Channel Attention and Multi-Detection Layer Anchor-Free Optimization
Su, Q., & Mu, J. (2024) · Automation · doi:10.3390/automation5020011 · Cited by 10
Foreign Object Detection Method in Transmission Lines Based on Improved YOLOv8n
Xu, W., Xiwen, C., Haibin, C., Cao, Y., & Zhang, J. (2024) · International Symposium on System Security, Safety, and Reliability (ISSSR) · doi:10.1109/isssr61934.2024.00029 · Cited by 9
“The current transmission line foreign object detection methods are mainly based on deep learning target detection as a framework, which contains two main categories, RCNN [2] and YOLO [3,4] series.”
Method
Flame and smoke detection using Kafka on edge devices
Chan, Y.-W., Liu, J., Kristiani, E., Lien, K.-Y., & Yang, C. (2024) · Internet of Things · doi:10.1016/j.iot.2024.101309 · Cited by 9
Rapid, autonomous and ultra-large-area detection of latent fingerprints using object-driven optical coherence tomography
He, B., Shi, Y., Sun, Z., Li, X., Hu, X., Wang, L., Xie, L., Yan, Y., Li, Z., Li, Z., et al. (2024) · Optics Express · doi:10.1364/oe.533554 · Cited by 9
Towards real-time monitoring of insect species populations
Venverloo, T., & Duarte, F. (2024) · Scientific Reports · doi:10.1038/s41598-024-68502-8 · Cited by 9
Two-step deep learning models for detection and identification of the manufacturers and types of dental implants on panoramic radiographs
Ariji, Y., Kusano, K., Fukuda, M., Wakata, Y., Nozawa, M., Kotaki, S., Ariji, E., & Baba, S. (2024) · Odontology · doi:10.1007/s10266-024-00989-z · Cited by 9
“It divides the entire image into grids, and predicts the bounding boxes and corresponding classes for each grid [21].”
Artificial Intelligence Vision Methods for Robotic Harvesting of Edible Flowers
Torre, F. T. D., Melgani, F., Pertot, I., & Furlanello, C. (2024) · Plants · doi:10.3390/plants13223197 · Cited by 9
Deep learning–based detection and quantification of weed seed mixtures
Ahmed, S., Revolinski, S. R., Maughan, P. J., Savic, M., Kalin, J. E. R., & Burke, I. C. (2024) · Weed Science · doi:10.1017/wsc.2024.60 · Cited by 9
“Several versions of YOLO have been released, with each model building upon addressing the limitations of its predecessor (Jiang et al. 2022).”
Syflo: augmenting yolo for real-time health monitoring of electric assets in power transmission lines
Sankuri, R. S., Sristy, N. B., & Karri, S. P. K. (2024) · Journal of Real-Time Image Processing · doi:10.1007/s11554-024-01566-x · Cited by 9
“Insulator defects often arise from mechanical deterioration or environmental factors, and traditional methods for detecting such issues-such as ultrasonic, acoustic, or radio interference techniques-are hampered by the need for close proximity and the presence of electrical interference [12].”
Design and Development of a Smart Pet Feeder with IoT and Deep Learning
Castillo-Arceo, O. E., Renteria-Flores, R. U., & Santana-Mancilla, P. C. (2024) · International Electronic Conference on Sensors and Applications (ECSA) · doi:10.3390/ecsa-11-20487 · Cited by 9
“The project uses a pet identification model based on YOLOv5, a cutting-edge deep-learning model known for its speed and accuracy in object detection [18,19].”
Faster R-CNN-based Detection and Tracking of Hydrogen and Oxygen Bubbles in Alkaline Water Electrolysis
TOYAMA, K., Kanemoto, R., Misumi, R., Araki, T., & Mitsushima, S. (2025) · Electrochemistry · doi:10.5796/electrochemistry.24-00127 · Cited by 9
Using deep learning model integration to build a smart railway traffic safety monitoring system
Chang, C., Huang, K.-H., Lau, T.-K., Huang, C.-F., & Wang, C.-H. (2025) · Scientific Reports · doi:10.1038/s41598-025-88830-7 · Cited by 9
“In recent years, advanced versions of YOLO were further developed, which improved and enhanced the performance of object detection 28 .”
A cyber-physical system based unmanned ground vehicles for safety inspection and rescue support in an underground mine
Behera, L., Agarwal, S., Sandhan, T., Sharma, P., Kumar, A., Ranjan, A., Watsa, S., Singh, A. P., & Swaroop, K. J. (2025) · International Journal of Intelligent Unmanned Systems · doi:10.1108/ijius-07-2024-0202 · Cited by 9
An empirical study of automatic wildlife detection using drone-derived imagery and object detection
Vuong, T., Chang, M., Palaparthi, M., Howell, L. G., Bonti, A., Abdelrazek, M., & Nguyen, D. T. (2025) · Multimedia Tools and Applications · doi:10.1007/s11042-024-20522-2 · Cited by 17
“For instance, YOLO has a long history of development with many variants corresponding to different amendments in the architecture (for both feature learning and object location prediction) [23].”
Machine learning for quality control of Tin-Copper electrodes
Nyabadza, A., Azoulay-Younes, L., Vázquez, M., & Brabazon, D. (2025) · Measurement · doi:10.1016/j.measurement.2025.117191 · Cited by 9
Combining and Decoupling Rigid and Soft Grippers to Enhance Robotic Manipulation
Keely, M., Kim, Y., Mehta, S. A., Hoegerman, J., Sanchez, R. R., Paul, E., Mills, C., Losey, D. P., & Bartlett, M. D. (2025) · Soft Robotics · doi:10.1089/soro.2024.0062 · Cited by 9
Study on lightweight rice blast detection method based on improved YOLOv8
Jin, S., Cao, Q., Li, J., Wang, X., Li, J., Feng, S., & Xu, T. (2025) · Pest Management Science · doi:10.1002/ps.8790 · Cited by 11

Authors: Jiang, P., Ergu, D., Liu, F., Cai, Y., & Ma, B.
Affiliations: Southwest Minzu University (CN)
Corresponding author: Ergu, Daji
ORCID: Ergu, Daji
Liu, Fangyao
Cai, Ying
Venue: Procedia Computer Science
Publisher: Elsevier BV
Published: 2022
Volume: 199
Pages: 1066-1073
ISSN: 1877-0509 (ISSN-L)
DOI: 10.1016/j.procs.2022.01.135
OpenAlex: W4210598935
Cited by: 2,783
References: 15
Citations by year: 2026: 474 · 2025: 880 · 2024: 879 · 2023: 464 · 2022: 86
FWCI: 209.36
Type: conference paper
Language: en
Open access: diamond
doi.org
Licence: cc-by-nc-nd
www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
creativecommons.org/licenses/by-nc-nd/4.0/ (vor, 10 d)
Locations: Procedia Computer Science (publishedVersion, cc-by-nc-nd)
Funders: Fundamental Research Funds for the Central Universities
Southwest Minzu University (2018NZD02, 2020NGD04)
National Natural Science Foundation of China (71774134, U1811462)
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 › Advanced Image and Video Retrieval Techniques
Keywords: YOLO, object detection, convolutional neural networks, feature selection, feature extraction, target recognition

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A Review of Yolo Algorithm Developments

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