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A Review of Yolo Algorithm Developments
Open accessAbstract
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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“Although several papers have reviewed YOLO architectures, they often exhibit limitations such as focusing on specific YOLO variants [10], [11], or concentrating on particular application domains [12].”
From YOLOv1 to v8: Unveiling Each Variant–A Comprehensive Review of YOLO (2024) · cites this paper as [10]
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Cites
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A Robust Real-Time Automatic License Plate Recognition Based on the YOLO Detector
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YOLO-LITE: A Real-Time Object Detection Algorithm Optimized for Non-GPU Computers
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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
A review of object detection based on convolutional neural network
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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
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Cited by
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Items 601–700 of 1,001
YOLO-MFX: lightweight YOLO with improved flame detection for small targets
Yao, Q., Xu, H., Feng, Y., Wang, X., & Zhang, C. (2025) · Journal of Real-Time Image Processing · doi:10.1007/s11554-025-01666-2 · Cited by 9
Dilmi, W., Ferik, S. E., Ouerdane, F., Khaldi, M. K., & Saif, A. A. (2025) · Sensors · doi:10.3390/s25082572 · Cited by 14
“In successive iterations, YOLOv4, YOLOv5 and YOLOv8 have incorporated enhancements such as anchor-free detection and transformer-based layers [6], further optimizing efficiency and precision.”
CBLN-YOLO: An Improved YOLO11n-Seg Network for Cotton Topping in Fields
Xie, Y., & Chen, L. (2025) · Agronomy · doi:10.3390/agronomy15040996 · Cited by 12
Liu, Y., Wang, C., Zhang, Y., Sun, X., Du, C., Xie, D., & Tian, Y. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.111246 · Cited by 9
Ma, Y., Al‐Aroomi, M. A., Zheng, Y., Ren, W., Liu, P., Wu, Q., Liang, Y., & Jiang, C. (2025) · BMC Oral Health · doi:10.1186/s12903-025-06293-8 · Cited by 9
“Mask R-CNN provides high segmentation accuracy but is slower than You Only Look Once (YOLO) [45], which is better suited for real-time detection, though it may sacrifice some precision.”
Bi, J.-W., Wei, Z., & Han, T.-Y. (2025) · Tourism Management · doi:10.1016/j.tourman.2025.105219 · Cited by 9
YOLOv8-DBW: An Improved YOLOv8-Based Algorithm for Maize Leaf Diseases and Pests Detection
Gan, X., Cao, S., Wang, J., Wang, Y., & Hou, X. (2025) · Sensors · doi:10.3390/s25154529 · Cited by 9
“Another category is the single-stage detection algorithms represented by SSD [22] and the YOLO series [23], which do not require the generation of candidate regions and have a significant advantage in detection speed [24].”
Detection of Surface Defects in Steel Based on Dual-Backbone Network: MBDNet-Attention-YOLO
Wang, X., Ma, S., Wu, S., Li, Z., Cao, J., & Xu, P. (2025) · Sensors · doi:10.3390/s25154817 · Cited by 9
Real‐Time Automatic Detection of Nutrient Deficiency in Lettuce Plants With New YOLOV12 model
Bakır, Ç., & Gezer, A. (2025) · Journal of Sensors · doi:10.1155/js/5592225 · Cited by 9
“The optimization algorithm determines how the model will update its weights [44].”
Jiang, T., Li, L., Zhang, Z., Yu, X., Zhu, Y., Li, L., Liu, Y.-D., Bai, Y., Tang, Z., Liu, S., et al. (2025) · Artificial Intelligence in Agriculture · doi:10.1016/j.aiia.2025.10.002 · Cited by 9
Eliwa, E. H. I., & El‐Hafeez, T. A. (2025) · Egyptian Informatics Journal · doi:10.1016/j.eij.2025.100851 · Cited by 9
Chaieb, M., Mayara, H., Hamdoun, W., Gabsi, M., & Hilali‐Jaghdam, I. (2025) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-025-02742-x · Cited by 9
“More recently, researchers have demonstrated that using deep learning models (e.g., VGG19, AlexNet, YOLOV, ResNet, etc.) that have already been trained or fine-tuned yields better results.”
InfluentialHan, Y.-S., Zhao, S.-L., Chen, C., Cui, K., Hu, P., & Wang, R.-F. (2026) · Journal of Marine Science and Engineering · doi:10.3390/jmse14040351 · Cited by 9
“The YOLO (You Only Look Once) family, as one of the most widely used single-stage detection frameworks [74,75], has been extensively adopted in agricultural target detection due to its simple architecture, fast detection speed, and competitive accuracy [25,46,73].”
Real-time Traffic Surveillance and Detection using Deep Learning and Computer Vision Techniques
Tiwari, R., Rumaney, A. H., & Saravanan, M. (2023) · International Conference on Vision Towards Emerging Trends in Communication and Networking (ViTECoN) · doi:10.1109/vitecon58111.2023.10157694 · Cited by 9
“The YOLOv5 and YOLOv7 models have been utilized for object detection and speed estimation, respectively [9].”
BackgroundMethodCrop and Weed Detection in Sunflower and Sugarbeet fields Using Single Shot Detectors
Abuhani, D. A., Hussain, M. H., Khan, J., ElMohandes, M., & Zualkernan, I. A. (2023) · IEEE International Conference on Omni-layer Intelligent Systems (COINS) · doi:10.1109/coins57856.2023.10189257 · Cited by 9
“The You Only Look Once (YOLO) class of architectures is well known for doing such tasks [6].”
BackgroundMiclăuş, S., Deaconescu, D. B., Vătămanu, D., & Buda, A. M. (2023) · Technologies · doi:10.3390/technologies11050113 · Cited by 11
“You Only Look Once (YOLO) is a deep learning algorithm that falls under the category of object detection in computer vision [50,51].”
BackgroundSlim, S. O., Abdelnaby, I. A., Moustafa, M. S., Zahran, M., Dahi, H., & Yones, M. (2023) · The Egyptian Journal of Remote Sensing and Space Science · doi:10.1016/j.ejrs.2023.10.001 · Cited by 9
Motorcycle Detection and Collision Warning Using Monocular Images from a Vehicle
Shabestari, Z. B., Ahmadabadian, A. H., & Fabio Remondino, A. (2023) · Remote Sensing · doi:10.3390/rs15235548 · Cited by 12
Ahmadpanah, H., Haidar, A., & Latifi, S. M. (2023) · eCAADe proceedings · doi:10.52842/conf.ecaade.2023.2.619 · Cited by 9
Li, G., Zhao, S., Li, M., Zhou, M., & Ying, Z. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.107677 · Cited by 9
Neural Network Based Data Encryption: A Comparison Study among DES, AES, and HE Techniques
Yeow, S.-Q., & Ng, K.-W. (2023) · JOIV International Journal on Informatics Visualization · doi:10.30630/joiv.7.3-2.2336 · Cited by 9
Object detection based deinterleaving of radar signals using deep learning for cognitive EW
Kocamış, M. B., Orduyılmaz, A., & Taşcıoğlu, S. (2024) · Signal Image and Video Processing · doi:10.1007/s11760-024-03428-2 · Cited by 9
METRIC: Toward a Drone-based Cyber-Physical Traffic Management System
Ma, X., Liang, X., Ning, M., & Radu, A. (2022) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc53654.2022.9945433 · Cited by 9
“The first essential component of METRIC is the SeeFar computer vision system [6], based on object detector YOLOv5 [7] and object tracker DeepSORT [8].”
MethodGhourabi, M., Mourad-Chehade, F., & Chkeir, A. (2023) · Sensors · doi:10.3390/s23041851 · Cited by 9
“In addition, YOLO can detect objects in videos, making it a better choice in real-time detection scenarios [11].”
Real Time Detection and Tracking in Multi Speakers Video Conferencing
Affes, N., Ktari, J., Amor, N. B., Frikha, T., & Hamam, H. (2023) · Lecture notes in networks and systems · doi:10.1007/978-3-031-35501-1_11 · Cited by 9
Training SAR Ship Detectors From Scratch with Customized Convolutional Neural Network
Li, J., Xu, C., Cheng, P., Chi, C., Yu, L., Yu, Z., & Xu, S. (2023) · IEEE Transactions on Aerospace and Electronic Systems · doi:10.1109/taes.2023.3308096 · Cited by 9
“On the other hand, single-stage methods mimic the principles of the human visual system, enabling them to output classification and localization information with a single forward computation [12], [13], [14], [15], [16], [17].”
MethodTaşyürek, M. (2024) · Journal of Real-Time Image Processing · doi:10.1007/s11554-024-01451-7 · Cited by 9
“Previous research [63, 64] has illustrated that the YOLOv5 model provides more precise estimations at a reduced computational cost compared to the V4 model.”
YOLOv10 to Its Genesis: A Decadal and Comprehensive Review of The You Only Look Once SeriesPreprint
Sapkota, R., Qureshi, R., Flores-Calero, M. J., Badgujar, C., Nepal, U., Poulose, A., Zeno, P. J., Vaddevolu, U. B. P., Yan, P. H., & Karkee, M. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4874098 · Cited by 9
Bian, H., Jiang, M., & Qian, J. (2023) · PLoS ONE · doi:10.1371/journal.pone.0288376 · Cited by 8
LED-Display Defect Detection Based on YOLOv5 and Transformer
Park, J., Bae, J., Lim, J., Kim, B., & Jeong, J. (2023) · IEEE Access · doi:10.1109/access.2023.3325487 · Cited by 8
“Interms of speed and accuracy, YOLOv5 acts better than its predecessors [8].”
BackgroundComparison Analysis of YOLO, ResNet, and EfficientNet for contagious skin diseases
Ferdinand, F., Austin, F., & Sadrawi, M. (2023) · International Conference on Cybernetics and Intelligent System (ICORIS) · doi:10.1109/icoris60118.2023.10352306 · Cited by 8
“YOLO was First published in 2015, and has multiple new versions throughout the years developed by multiple different people [1].”
BackgroundOutdoor Obstacle Detection for Visually Impaired using AI Technique
Bougheloum, L., Salah, M. B., & Bettayeb, M. (2024) · ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS) · doi:10.1109/icetsis61505.2024.10459374 · Cited by 8
“YOLO approaches object detection [20] as a regression problem, directly predicting bounding boxes and class probabilities for multiple objects in a single pass through the neural network.”
MethodAn efficient center-based method for real-time pig posture recognition and tracking
Mattina, M., Benzinou, A., Nasreddine, K., & Richard, F. (2024) · Applied Intelligence · doi:10.1007/s10489-024-05439-5 · Cited by 9
“Shao et al. [15] use the YOLOv5 detection model [41] to detect pigs: each cropped image is extracted and then fed into a CNN trained to classify the animal’s posture (standing, sitting, or lying down).”
Liu, P., Yang, J., Liu, C., Zhao, Y., & hu, Z. (2024) · Measurement Science and Technology · doi:10.1088/1361-6501/ad56ae · Cited by 8
“The YOLO algorithm is applied in this study to classify rail corrugation wavelengths, improving the effective detection and categorization of different rail corrugation wavelengths [37].”
Pose estimation algorithm based on point pair features using PointNet + +
Chen, Y., Li, Z., Li, Q., & Zhang, M. (2024) · Complex & Intelligent Systems · doi:10.1007/s40747-024-01508-x · Cited by 8
“Mainstream deep learning algorithms for object detection primarily fall into two categories: one-stage algorithms, with YOLO [7] as a representative example, and two-stage algorithms, with CNN [8, 9] as representative.”
Side-Scan Sonar Image Generator Based on Diffusion Models for Autonomous Underwater Vehicles
Zhang, F., Hou, X., Wang, Z., Cheng, C., & Tan, T. (2024) · Journal of Marine Science and Engineering · doi:10.3390/jmse12081457 · Cited by 9
Wu, M., Yuan, K., Shui, Y., Wang, Q., & Zhao, Z. (2024) · Agronomy · doi:10.3390/agronomy14081835 · Cited by 8
“The YOLO series, as a representative of deep learning networks, is widely used to detect and count fruits and vegetables [16].”
BackgroundMartinez, F., Romaine, J. B., Manzano, J. M., Ierardi, C., & Millán, P. (2024) · IEEE Access · doi:10.1109/access.2024.3453993 · Cited by 11
“This metric provides a balanced evaluation of precision and recall by considering the area under the precision-recall curve [54], [55] and is calculated as follows : where TP are the true positives, FP the false positives, and FN the false negatives.”
MethodFMAW-YOLOv5s: A deep learning method for detection of methane plumes using optical images
Zhang, Q., Bi, S., Xie, Y., & Liu, G. (2024) · Applied Ocean Research · doi:10.1016/j.apor.2024.104217 · Cited by 8
A novel method: YOLO-CE and 3D point cloud-based feature extraction for welding seams of tower bases
Pan, H., Wang, L., Fu, Y., Jia, B., & Chen, L. (2024) · Measurement Science and Technology · doi:10.1088/1361-6501/ad89e8 · Cited by 8
“Developed by Joseph Redmon and Ali Farhadi, YOLO frames the object detection problem as a single regression problem, directly predicting bounding boxes and class probabilities from full images in one evaluation [23].”
Franco-González, I. T., Lappalainen, N., & Bednarik, R. (2024) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2024.101593 · Cited by 8
Adolescent mental health state assessment framework by combining YOLO with random forest
Wan, M., & Zou, S. (2024) · Applied Soft Computing · doi:10.1016/j.asoc.2024.112497 · Cited by 8
Enhancing pollinator conservation: Monitoring of bees through object recognition
Alex, A. J., Barnes, C. M., Machado, P., Ihianle, I. K., Markó, G., Bencsik, M., & Bird, J. J. (2024) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2024.109665 · Cited by 8
Benchmarking YOLO Models for Intracranial Hemorrhage Detection Using Varied CT Data Sources
Tapia, G., Allende‐Cid, H., Chabert, S., Mery, D., & Rodrigo Salas, F. (2024) · IEEE Access · doi:10.1109/access.2024.3510517 · Cited by 8
“In 2018, YOLOv3 [14] presented a new backbone network [15].”
Corn pose estimation using 3D object detection and stereo images
Gao, Y.-L., Li, Z., Hong, Q.-Q., Li, B., & Zhang, L.-F. (2025) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2025.110016 · Cited by 8
Wang, Z., Cui, W., Huang, C., Zhou, Y., Zhao, Z., Yue, Y., Dong, X., & Lv, C. (2025) · Agriculture · doi:10.3390/agriculture15030305 · Cited by 9
Intelligent fault diagnosis of rolling bearing based on time–frequency processing and DLKA-YOLO
Chen, Z., Zhang, H., Tao, Z., & Wang, Y. (2025) · Franklin Open · doi:10.1016/j.fraope.2025.100228 · Cited by 8
MDD-YOLOv8: A Multi-Scale Object Detection Model Based on YOLOv8 for Synthetic Aperture Radar Images
Liu, J., Liu, X., Chen, H., & Luo, S. (2025) · Applied Sciences · doi:10.3390/app15042239 · Cited by 10
“The YOLO algorithm series is renowned for its speed and accuracy in performing object detection and image segmentation tasks [27,28].”
An efficient lightweight detection model for steel surface defects with dynamic deformable head
Li, C., Wen, Z., Huang, H., Mo, H., Zhou, S., & Zhu, Z. (2025) · Engineering Research Express · doi:10.1088/2631-8695/adbab4 · Cited by 8
IMRMB-Net: A lightweight student behavior recognition model for complex classroom scenarios
Feng, C., Luo, Z., Kong, D., Ding, Y., & Liu, J. (2025) · PLoS ONE · doi:10.1371/journal.pone.0318817 · Cited by 9
“In order to forecast the object’s location and class, these techniques employ direct global regression on the image [22].SSD techniques can handle objects of various sizes and can be detected at several scales [23].”
Tennekoon, S., Wedasingha, N., Welhenge, A. M., Abhayasinghe, N., & Murray, I. D. (2025) · IEEE Access · doi:10.1109/access.2025.3551686 · Cited by 8
Forest Fire Detection Based on Enhanced Feature Information Capture and Long-Range Dependency
Lin, W., Gao, J., & Huang, J. (2025) · IEEE Access · doi:10.1109/access.2025.3560192 · Cited by 10
“Furthermore, Li and Zhao [9] evaluated various models including Faster R-CNN, R-FCN [10], SSD, and YOLOv3 [11].”
Zade, F. A., Abedini, R., Abdullah, A., & Passandideh‐Fard, M. (2025) · Journal of Thermal Analysis and Calorimetry · doi:10.1007/s10973-025-14250-4 · Cited by 8
“A convolutional layer applies a convolution operation to the input which helps the network learn location-invari-ant features [40, 41].”
Deep Learning-Based Classification of Canine Cataracts from Ocular B-Mode Ultrasound Images
Park, S., Go, S., Kim, S., & Shim, J. (2025) · Animals · doi:10.3390/ani15091327 · Cited by 12
“%) using a YOLO-v3 and DenseNet-161 combination for cataract detection [1]; however, YOLO-based methods require labor-intensive manual labeling of bounding boxes [33,34].”
Malashin, I. P., & Martysyuk, D. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.128001 · Cited by 8
Automated Detection and Localization of Wind Turbine Deformations Through YOLO11 and UAV Systems
Bakırcı, M., & Avran, M. N. (2025) · International Russian Smart Industry Conference (SmartIndustryCon) · doi:10.1109/smartindustrycon65166.2025.10986124 · Cited by 8
“Simultaneously, advancements in AI-based object detection algorithms, such as YOLO, have significantly enhanced the precision and speed of image analysis in a range of industrial applications [11-14].”
Shen, X., Li, S., Fen, Q., & Yao, L. (2025) · Smart Agricultural Technology · doi:10.1016/j.atech.2025.101055 · Cited by 8
Computationally Enhanced UAV-Based Real-Time Pothole Detection Using YOLOv7-C3ECA-DSA Algorithm
Radzi, S. F. M., Rahman, M. A. A., Yusof, K. A., Haniff, N. S. M., & Rahmat, R. F. (2025) · IEEE Access · doi:10.1109/access.2025.3573651 · Cited by 8
“YOLOv5 [23] has markedly reduced in size relative to YOLOv4 [24].”
Chen, Y., Xu, B., Jiang, Y., Xu, Z., Wang, X., Liu, T., & Zhu, W. (2025) · Automation in Construction · doi:10.1016/j.autcon.2025.106368 · Cited by 8
Seamless optical cloud computing across edge-metro network for generative AI
Xing, S., Sun, A., Wang, C., Wang, Y., Dong, B., Hu, J., Deng, X., Yan, A., Liu, Y., Hu, F., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-61495-6 · Cited by 8
“The rapid advancement of generative arti fi cial intelligence (AI) in recent years has profoundly reshaped modern lifestyles, necessitating a revolutionary architecture to support the growing demands for computational power.”
Lei, J., Wang, J., Li, Y., Zhang, M., Zhao, R., & Sun, H. (2025) · Measurement Science and Technology · doi:10.1088/1361-6501/aded2b · Cited by 8
“Deep learning-based surface defect detection algorithms are further categorized into two-stage approaches (represented by RCNN [5], Mask R-CNN [6], and Faster R-CNN [7]) and single-stage detection algorithms (exemplified by SSD [8]and the YOLO series [9, 10].”
Ameur, S., Tabaa, M., Hidila, Z., Hamlich, M., Karboub, K., & Béarée, R. (2025) · Frontiers in Robotics and AI · doi:10.3389/frobt.2025.1584657 · Cited by 8
Benchmarking YOLOv8–YOLOv12 for Real-Time Object Detection on Single-Board Computers
Shalash, O., Khatab, E., El-Agamy, A., Elmokadem, L., Abouelsaad, Y., Zaki, J., El-Sayed, M., & Said, H. (2026) · Machine Learning and Knowledge Extraction · doi:10.3390/make8070204 · Cited by 8
“There are many comprehensive reviews on YOLO models: • In [26], the authors reviewed YOLOv1 through YOLOv5, analyzing their architectures, detection speed, accuracy, and dataset compatibility.”
Blood Cells Counting and Localisation through Deep Learning Object Detection
Mercaldo, F., Martinelli, F., Santone, A., & Cesarelli, M. (2022) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata55660.2022.10020952 · Cited by 9
“…YOLO makes more localization errors [22], at the same time is less likely to recognize false-positives in the background of the image, as well as being significantly faster [23], [19]: this is the reason why YOLO is considered one of the best convolutional neural network model for object detection.”
BackgroundTraffic Sign Detection and Recognition Using YOLOv5 and Its Versions
Lebumfacil, A., & Abu, P. A. R. (2022) · IEEE International Conference on Cognitive Mobility (CogMob) · doi:10.1109/cogmob55547.2022.10118112 · Cited by 8
Yang, X., Ji, W., Zhang, S., Song, Y., He, L., & Xue, H. (2023) · Journal of Real-Time Image Processing · doi:10.1007/s11554-023-01323-6 · Cited by 8
“These papers [16–19] are an anchor-based lane detection algorithm, similar to the You Only Live Once (YOLO) [20] series or the single-stage model of Single Shot MultiBox Detector (SSD) [21], which uses a target detector to detect the anchor frame region and fit the anchor frame belonging to the lanes.”
MethodChatterjee, R., & Chatterjee, A. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-16441-3 · Cited by 9
RetractedFerdinand, J., Chow, D. V., & Prasetyo, S. Y. (2023) · E3S Web of Conferences · doi:10.1051/e3sconf/202342601076 · Cited by 8
Meng, Y., Song, Y., Chen, Y., Zhang, X., Wu, M., & Du, B. (2023) · IET Cyber-Physical Systems Theory & Applications · doi:10.1049/cps2.12073 · Cited by 8
Sun, K., Huo, J., Liu, Q., & Yang, S. (2023) · Mathematical Biosciences & Engineering · doi:10.3934/mbe.2023842 · Cited by 8
Lee, C., Schätzle, S., Lang, S. A., & Oksanen, T. (2023) · Smart Agricultural Technology · doi:10.1016/j.atech.2023.100346 · Cited by 8
Yang, J. S., Mendili, L. E., Khayer, Y., McArdle, S., & Beni, L. H. (2023) · The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprs-archives-xlviii-1-w2-2023-203-2023 · Cited by 8
“In terms of ITC detection and segmentation, Convolutional Neural Network (CNN) based architectures play a dominant role, including YOLO (Jiang et al. 2022) and Mask R-CNN (He et al. 2017).”
BackgroundHa, J.-U., Kim, H., Cho, M., & Lee, M. (2023) · Electronics · doi:10.3390/electronics13010038 · Cited by 8
Multiview-based method for high-throughput quality classification of germinated oil palm seeds
Ng, J., Liao, I. Y., Jelani, M. F., Chen, Z. Y., Wong, C. K., & Wong, W. C. (2024) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2024.108684 · Cited by 8
Feng, Z.-C., Yang, J., Li, F., Chen, Z., Kang, Z., & Jia, L. (2024) · Journal of Electrical Engineering and Technology · doi:10.1007/s42835-024-01805-y · Cited by 8
“In the encoder, the Focus module [35] enables downsampling without information loss, and the sandglass unit [41] can encode features efficiently with fewer parameters.”
Dong, K., Liu, T., Shi, Z., & Zhang, Y. (2024) · Journal of Real-Time Image Processing · doi:10.1007/s11554-024-01417-9 · Cited by 9
Real-Time Reconstruction of 3-D Tactile Motion Field via Multitask Learning
Liu, J., Yu, H., Zhao, C., Liu, W., Ma, D., & Wang, W. (2024) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2024.3398136 · Cited by 8
“The model uses a YOLO-like encoder [37] with the C2 module as the feature extraction block, a choice made for the balance of accuracy and performance.”
BackgroundMethodHeuristic data-driven anchor generation for UAV-based maritime rescue image object detection
Zhao, B., Song, R., Zhou, Y., Yu, L., Zhang, X., & Liu, J. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e30485 · Cited by 10
“Existing state-of-the-art object detection models can be categorized into two groups based on the use of anchor boxes: those relying on anchor boxes [14,13,30,24,15,22] and anchor-free models [19,9,32,43,2].”
BackgroundUAV and Deep Learning: Detection of selected riparian species along the Ganga River
Tripathi, R. N., Ramachandran, A., Agarwal, K., Tripathi, V., Badola, R., & Hussain, S. A. (2024) · The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprs-archives-xlviii-1-2024-637-2024 · Cited by 9
A Comprehensive Review of Knowledge Distillation- Methods, Applications, and Future Directions
Zhu, E. Y., Zhao, C., Yang, H., Li, J., Wu, Y., & Ding, R. (2024) · International Journal of Innovative Research in Computer Science & Technology · doi:10.55524/ijircst.2024.12.3.17 · Cited by 8
Object detection algorithms to identify skeletal components in carbonate cores
Dawson, H. L., & John, C. M. (2024) · Marine and Petroleum Geology · doi:10.1016/j.marpetgeo.2024.106965 · Cited by 8
Kim, K. H., Koo, H., & Lee, B. (2024) · Korean Journal of Neurotrauma · doi:10.13004/kjnt.2024.20.e17 · Cited by 8
“15) The model then refines these anchor boxes to predict the actual bounding boxes of objects in the image.”
BackgroundCai, J., Hu, W., Yan, H., Wang, N., Hong, M., Xing, X., & Zhang, Y. (2024) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2024.3445604 · Cited by 8
Research on the Detection of Steel Plate Defects Based on SimAM and Twin-NMF Transfer
Zou, Y., Zhang, G., & Fan, Y. (2024) · Mathematics · doi:10.3390/math12172782 · Cited by 8
A method for maize pest detection based on improved YOLO-v9 model
Li, Y., Wang, M., Wang, C., & Zhong, M. (2024) · International Conference on Computer Information Science and Application Technology (CISAT) · doi:10.1109/cisat62382.2024.10695307 · Cited by 8
“Popular object detection networks include Faster R-CNN [1], SSD [2], and the YOLO (You Only Look Once) series [3].”
MethodBakırcı, M., & Bayraktar, I. (2024) · International Russian Automation Conference (RusAutoCon) · doi:10.1109/rusautocon61949.2024.10694108 · Cited by 8
“Among these algorithms, the You Only Look Once (YOLO) algorithm has emerged as a frontrunner, particularly renowned for its single-stage object detection capabilities [15].”
MethodUAV measurements and AI-driven algorithms fusion for real estate good governance principles support
Tysiąc, P., Janowski, A., & Walacik, M. (2024) · International Journal of Applied Earth Observation and Geoinformation · doi:10.1016/j.jag.2024.104229 · Cited by 8
Research on a Target Detection Algorithm for Common Pests Based on an Improved YOLOv7-Tiny Model
Gong, H., Ma, X., & Guo, Y. (2024) · Agronomy · doi:10.3390/agronomy14123068 · Cited by 8
Caballero, J. (2023) · Hipertext net Revista Académica sobre Documentación Digital y Comunicación Interactiva · doi:10.31009/hipertext.net.2023.i26.08 · Cited by 12
Design a Robust Real-Time Trash Detection System Using YOLOv5 Variants
Jalal, S. I., Ahmed, H. A., & Ahmed, M. H. (2023) · IEEE IAS Global Conference on Emerging Technologies (GlobConET) · doi:10.1109/globconet56651.2023.10149899 · Cited by 8
A study on phenotypic micro-variation of stored melon based on weight loss rate
Qian, C., Sun, S., Dong, C., Chen, C., Liu, W., & Du, T. (2023) · Postharvest Biology and Technology · doi:10.1016/j.postharvbio.2023.112464 · Cited by 8
Computer vision with smartphone microphotography for detection of carious lesions
Farook, T. H., Ahmed, S., Jamayet, N. B., & Dudley, J. (2023) · Intelligence-Based Medicine · doi:10.1016/j.ibmed.2023.100105 · Cited by 8
An Integrated Framework for Bird Recognition Using Dynamic Machine Learning-Based Classification
Rabhi, W., Eljaimi, F., Amara, W., Charouh, Z., Ezzouhri, A., Benaboud, H., Saindou, M. B., & Ouardi, F. (2023) · IEEE Symposium on Computers and Communications (ISCC) · doi:10.1109/iscc58397.2023.10218182 · Cited by 8
“Recently, deep learning-based object detection algorithms, such as YOLO [2], have shown great promise in detecting objects in images and videos [3], [4].”
BackgroundInfluentialDevelopment of Identification System for Cactaceae Plants using YOLO Algorithm
Leynes, L. J. D., Razon, I. C. L., & Caya, M. V. C. (2023) · IEEE Eurasia Conference on IOT, Communication and Engineering (ECICE) · doi:10.1109/ecice59523.2023.10383190 · Cited by 8
“Regarding real-time object detection, the You Only Look Once (YOLO) algorithm employs CNN [3].”
MethodObject Detection in Autonomous Vehicles
Fakharurazi, M. I. M., Jusoh, A. Z., Asnawi, A. L., Malek, N. F. A., Abdullah, K., & Azmin, N. F. M. (2023) · IEEE International Conference on System Engineering and Technology (ICSET) · doi:10.1109/icset59111.2023.10295116 · Cited by 8
“Redmon et. al introduced YOLO in 2015 [6].”
BackgroundMoussaoui, H., AKKAD, N. E., & BENSLIMANE, M. (2023) · Research Square · doi:10.21203/rs.3.rs-3195386/v1 · Cited by 8
Traffic Flow Optimization Using CNN and GCN for Adaptive Signal Control
Kolte, S., Ingle, A., Kshirsagar, K. P., Gawande, P. G., Kumbhar, K. A., & Kawchale, R. (2023) · Asian Conference on Innovation in Technology (ASIANCON) · doi:10.1109/asiancon58793.2023.10269944 · Cited by 8
Tsai, Y.-L. S., Huang, C.-J., Chen, C.-L., & Han, J.-Y. (2023) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2023.3337126 · Cited by 7
Rice Ears Detection Method Based on Multi-Scale Image Recognition and Attention Mechanism
Fen, Q., Shen, X., Zhou, C., He, W., & Yao, L. (2024) · IEEE Access · doi:10.1109/access.2024.3400254 · Cited by 7
“Current deep learning models for target detection are mainly categorized into regression-based single-stage models (e.g., Single Shot MultiBox Detector, You Only Look Once) [14], [15] and candidate region-based two-stage models (e.g., R-CNN, Fast-RCNN) [16], [17].”
MethodDevelopment of a Lightweight Floating Object Detection Algorithm
Xian, R., Tang, L., & Liu, S. (2024) · Water · doi:10.3390/w16111633 · Cited by 7
“Single-stage object detection algorithms: To further improve the speed of object detection, researchers have proposed single-stage object detection algorithms such as YOLO (You Only Look Once) [7] and SSD (Single-Shot MultiBox Detector) [8].”
Method
Metadata
- Authors
- Jiang, P., Ergu, D., Liu, F., Cai, Y., & Ma, B.
- Affiliations
- Southwest Minzu University (CN)
- Corresponding author
- Ergu, Daji
- Venue
- Procedia Computer Science
- Publisher
- Elsevier BV
- Published
- 2022
- Volume
- 199
- Pages
- 1066-1073
- ISSN
- 1877-0509 (ISSN-L)
- 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
- 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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