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

The DOI matches its Crossref record; 15 of the 15 references it cites were found in a registry or an index.

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

Open this paper in CoChat — free account Open PDF

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.

Integrity signals

Registry

Crossref · 10.1016/j.procs.2022.01.135

Cites

15 of 15 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

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?

You 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
YOLOv4: Optimal Speed and Accuracy of Object Detection
Bochkovskiy, A., Wang, C.-Y., & Liao, H.-Y. M. (2020) · arXiv · doi:10.48550/arxiv.2004.10934 · Cited by 10,307
Object Detection With Deep Learning: A Review
Zhao, Z.-Q., Zheng, P., Xu, S.-T., & Wu, X. (2019) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2018.2876865 · Cited by 5,545
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
A review of object detection based on convolutional neural network
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

Show all 15 references

Sorted by citations

Items 201–300 of 1,001

Optimizing YOLOv11 for automated classification of breast cancer in medical images
El‐Hafeez, T. A., Tarek, M., & Sayed, A. A. (2025) · Scientific Reports · doi:10.1038/s41598-025-24850-7 · Cited by 28
A Comprehensive Systematic Review of YOLO for Medical Object Detection (2018 to 2023)Preprint
Qureshi, R., Ragab, M. G., Abdulkadir, S. J., Muneer, A., ALQUSHAIB, A., Sumiea, E. H., & Alhussian, H. (2023) · doi:10.36227/techrxiv.23681679 · Cited by 31
Fast and Non-Destructive Quail Egg Freshness Assessment Using a Thermal Camera and Deep Learning-Based Air Cell Detection Algorithms for the Revalidation of the Expiration Date of Eggs
Nakaguchi, V. M., & Ahamed, T. (2022) · Sensors · doi:10.3390/s22207703 · Cited by 27
“Over time, the upgraded versions have stepped-up the tradeoff between speed and accuracy of detections, and besides that, from the version 5 (referred to as YOLOv5), the YOLO algorithms have incorporated user-friendly characteristics, including less complex framework, training efficiency, and portability of models amo…”
Background
Review on image-based animals weight weighing
Zhao, Y., Xiao, Q., Li, J., Tian, K., Yang, L., Shan, P., Lv, X., Li, L., & Zhan, Z. (2023) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2023.108456 · Cited by 32
UAV imagery coupled deep learning approach for the development of an adaptive in-house web-based application for yield estimation in citrus orchard
Subeesh, A., Kumar, S. P., Chakraborty, S. K., Upendar, K., Chandel, N. S., Jat, D., Dubey, K., Modi, R. U., & Khan, M. M. (2024) · Measurement · doi:10.1016/j.measurement.2024.114786 · Cited by 27
Real-time evaluation of object detection models across open world scenarios
Goswami, P., Aggarwal, L., Kumar, A., Kanwar, R., & Vasisht, U. (2024) · Applied Soft Computing · doi:10.1016/j.asoc.2024.111921 · Cited by 27
Crack instance segmentation using splittable transformer and position coordinates
Zhao, Y., Li, W., Ding, J., Wang, Y., Pei, L., & Tian, A. (2024) · Automation in Construction · doi:10.1016/j.autcon.2024.105838 · Cited by 28
DEP-Former: Multimodal Depression Recognition Based on Facial Expressions and Audio Features via Emotional Changes
Ye, J., Yu, Y., Lu, L., Wang, H., Zheng, Y., Liu, Y., & Wang, Q. (2024) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2024.3491098 · Cited by 33
“When the EWRE extends beyond the confines of laboratory data collection to more complex real-world scenarios, employing the YOLO series models [32] becomes a promising and effective solution.”
Machine Vision Applications for Welfare Monitoring in Aquaculture: Challenges and Opportunities
Fitzgerald, A., Ioannou, C. C., Consuegra, S., Dowsey, A. W., & de Leániz, C. G. (2025) · Aquaculture Fish and Fisheries · doi:10.1002/aff2.70036 · Cited by 30
DCFA-YOLO: A Dual-Channel Cross-Feature-Fusion Attention YOLO Network for Cherry Tomato Bunch Detection
Chai, S., Wen, M., Li, P., Zeng, Z., & Tian, Y. (2025) · Agriculture · doi:10.3390/agriculture15030271 · Cited by 27
“Typical single-stage detection methods include the YOLO series and the SSD network [4–6].”
A Systematic Review of YOLO-Based Object Detection in Medical Imaging: Advances, Challenges, and Future Directions
Cai, Z., Zhou, K.-Q., & Liao, Z. (2025) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2025.067994 · Cited by 38
Vision-Based Detection and Classification of Used Electronic Parts
Chand, P., & Lal, S. P. (2022) · Sensors · doi:10.3390/s22239079 · Cited by 31
Joint Source-Channel Coding System for 6G Communication: Design, Prototype and Future Directions
Zhong, X., Sham, C., Ma, L., Chou, H.-F., Mostaani, A., Vu, T. X., & Chatzinotas, S. (2024) · IEEE Access · doi:10.1109/access.2024.3360003 · Cited by 27
“This accelerator is designed to handle matrix multiplication, convolution, and activation functions, which are fundamental to neural network computations based on YOLO [90] and auto-encoder.”
Maturity Recognition and Fruit Counting for Sweet Peppers in Greenhouses Using Deep Learning Neural Networks
Escamilla, L. D. V., Gómez-Espinosa, A., Cabello, J. A. E., & Cantoral-Ceballos, J. A. (2024) · Agriculture · doi:10.3390/agriculture14030331 · Cited by 27
“YOLOv5 was picked because it is accurate, small, and stable [33].”
Background
Computer Vision-Based Real-Time Identification of Vehicle Loads for Structural Health Monitoring of Bridges
Yang, J., Bao, Y., Sun, Z., & Meng, X. (2024) · Sustainability · doi:10.3390/su16031081 · Cited by 26
“R-CNN and YOLO series are representative algorithms in this field, wherein innovative network structures such as SPPnet, FPN and PANnet are now playing a major role [23,24].”
Background
Flexible and Fully Quantized Lightweight TinyissimoYOLO for Ultra-Low-Power Edge Systems
Moosmann, J., Müller, H., Zimmerman, N., Rutishauser, G., Benini, L., & Magno, M. (2024) · IEEE Access · doi:10.1109/access.2024.3404878 · Cited by 35
“Restrictions apply.”
YOLOv8 Model for Weed Detection in Wheat Fields Based on a Visual Converter and Multi-Scale Feature Fusion
Liu, Y., Zeng, F., Diao, H., Zhu, J., Ji, D., Liao, X., & Zhao, Z. (2024) · Sensors · doi:10.3390/s24134379 · Cited by 26
“YOLO is extensively utilized in the ﬁ eld of agriculture [29,30].”
Method
Automatic mileage positioning for road inspection using binocular stereo vision system and global navigation satellite system
Luo, W., Qin, Y., Li, Q., Zhang, D., & Li, L. (2022) · Automation in Construction · doi:10.1016/j.autcon.2022.104705 · Cited by 26
Measuring Comfort Behaviours in Laying Hens Using Deep-Learning Tools
Sozzi, M., Pillan, G., Ciarelli, C., Marinello, F., Pirrone, F., Bordignon, F., Bordignon, A., Xiccato, G., & Trocino, A. (2022) · Animals · doi:10.3390/ani13010033 · Cited by 33
Green Fruit Detection with a Small Dataset under a Similar Color Background Based on the Improved YOLOv5-AT
Fu, X., Zhao, S., Wang, C., Tang, X., Tao, D., Li, G., Jiao, L., & Dong, D. (2024) · Foods · doi:10.3390/foods13071060 · Cited by 27
“The YOLO series algorithm, as a representative framework that derives its single-stage detection from YOLOv1-YOLOv8 (Terven et al. [16]), has the advantages of a more flexible structure, faster inference speed and stronger versatility compared to other object detection models (Jiang et al. [17]).”
Method
Improved Tomato Disease Detection with YOLOv5 and YOLOv8
Ahmed, R., & Abd-Elkawy, E. H. (2024) · Engineering Technology & Applied Science Research · doi:10.48084/etasr.7262 · Cited by 26
“Among these algorithms, two-stage approaches such as R-CNN involve regional proposal and classification stages, and one-stage approaches, such as SSD [6-7] and YOLO [8], perform detection in a single step.”
Method
Underwater fish detection and counting using image segmentation
Zhang, L., Qiu, Y., Fan, J., Li, S., Hu, Q., Xing, B., & Xu, J. (2024) · Aquaculture International · doi:10.1007/s10499-024-01402-w · Cited by 25
“It can be deployed easily on edge computing platforms in the field (Jiang et al. 2022).”
Maize tassel number and tasseling stage monitoring based on near-ground and UAV RGB images by improved YoloV8
Yu, X. B., Yin, D., Xu, H., Pinto, F., Schmidhalter, U., Nie, C., Bai, Y., Sankaran, S., Ming, B., Cui, N., et al. (2024) · Precision Agriculture · doi:10.1007/s11119-024-10135-y · Cited by 27
“The Yolo family has witnessed massive developments, with major breakthroughs in both model accuracy and efficiency (Jiang et al., 2022).”
Enhancing Livestock Detection: An Efficient Model Based on YOLOv8
Fang, C., Li, C., Yang, P., Kong, S., Han, Y., Huang, X., & Niu, J. (2024) · Applied Sciences · doi:10.3390/app14114809 · Cited by 30
GM-DETR: Research on a Defect Detection Method Based on Improved DETR
Liu, X., Yang, X., Shao, L., Wang, X., Gao, Q., & Shi, H. (2024) · Sensors · doi:10.3390/s24113610 · Cited by 29
“The classical object detection networks include the SSD [4], Faster R-CNN [5], and YOLO [6] series, among others.”
Method
sat2shp: Extracting key building features from a single satellite image for urban building energy modelling and beyond
Wang, T., Reinhart, C. F., & Ang, Y. Q. (2024) · Sustainable Cities and Society · doi:10.1016/j.scs.2024.106054 · Cited by 25
Smart robotic system guided with YOLOv5 based machine learning framework for efficient herbicide usage in rice (Oryza sativa L.) under precision agriculture
Mohanty, T., Pattanaik, P., Dash, S., Tripathy, H. P., & Holderbaum, W. (2025) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2025.110032 · Cited by 25
Real time intelligent garbage monitoring and efficient collection using Yolov8 and Yolov5 deep learning models for environmental sustainability
Abo-Zahhad, M. M., & Abo-Zahhad, M. M. (2025) · Scientific Reports · doi:10.1038/s41598-025-99885-x · Cited by 25
A robust deep learning pipeline for multi-class cervical cancer cell identification
Eliwa, E. H. I., & El‐Hafeez, T. A. (2025) · Egyptian Informatics Journal · doi:10.1016/j.eij.2025.100787 · Cited by 25
Deep learning model for automated detection of efflorescence and its possible treatment in images of brick facades
García, D. M., Bienvenido-Huertas, D., Carretero-Ayuso, M. J., & Torre, S. D. (2022) · Automation in Construction · doi:10.1016/j.autcon.2022.104658 · Cited by 28
Early and on-ground image-based detection of poppy (Papaver rhoeas) in wheat using YOLO architectures
Porras, F. P., Torres‐Sánchez, J., Granados, F. L., & Mesas‐Carrascosa, F. J. (2022) · Weed Science · doi:10.1017/wsc.2022.64 · Cited by 26
Enhancing two-stage object detection models via data-driven anchor box optimization in UAV-based maritime SAR
Zhao, B., & Song, R. (2024) · Scientific Reports · doi:10.1038/s41598-024-55570-z · Cited by 24
Geometrical quality inspection in 3D concrete printing using AI-assisted computer vision
Cui, W., Liu, W., Guo, R., Wan, D., Yu, X., Ding, L., & Tao, Y. (2025) · Materials and Structures · doi:10.1617/s11527-025-02594-0 · Cited by 24
“Momentum adjustment addresses the issue of slow optimization due to small learning rates causing oscillations in SGD, thus speeding up training and improving model stability [36].”
Influential
Traffic load simulation on bridges based on improved non-contact machine vision technique and multi-source information fusion
Yin, X., Huang, Z., & Liu, Y. (2023) · Measurement · doi:10.1016/j.measurement.2023.112754 · Cited by 24
An Improved Mineral Image Recognition Method Based on Deep Learning
Tang, H., Wang, H., Wang, L., Cao, C., Nie, Y., & Liu, S. (2023) · JOM · doi:10.1007/s11837-023-05792-9 · Cited by 25
Deep learning based atomic defect detection framework for two-dimensional materials
Chen, F. R., Lin, C., Siao, H.-Y., Jian, C.-Y., Yang, Y.-C., & Lin, C. (2023) · Scientific Data · doi:10.1038/s41597-023-02004-6 · Cited by 24
Transfer learning with YOLOV8 for real-time recognition system of American Sign Language Alphabet
Alsharif, B., Alalwany, E., & Ilyas, M. (2024) · Franklin Open · doi:10.1016/j.fraope.2024.100165 · Cited by 27
GLS-YOLOv8n: a lightweight ‘Guiqi’ mango detection model via RGB-depth-thermal image fusion
Cao, W., Zhang, Z., Wang, Z., Wei, A., Chen, Q., Igathinathane, C., Zhang, F., Abdelhamid, M. A., Ye, D., & Ampatzidis, Y. G. (2025) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2025.111355 · Cited by 24
Deep-Learning-Based Trunk Perception with Depth Estimation and DWA for Robust Navigation of Robotics in Orchards
Huang, P., Huang, P., Wang, Z., Wu, X. B., Liu, J., & Zhu, L. (2023) · Agronomy · doi:10.3390/agronomy13041084 · Cited by 23
A Review and Comparative Study of Explainable Deep Learning Models Applied on Action Recognition in Real Time
Mahmoudi, S. A., Amel, O., Stassin, S., Liagre, M., Benkedadra, M., & Mancaş, M. (2023) · Electronics · doi:10.3390/electronics12092027 · Cited by 24
RepVGG-YOLOv7: A Modified YOLOv7 for Fire Smoke Detection
Chen, X., Xue, Y., Hou, Q., Fu, Y., & Zhu, Y. (2023) · Fire · doi:10.3390/fire6100383 · Cited by 23
“One-stage algorithms are characterized by high speed, simple models and easy deployment, and their representative algorithms are the YOLO series and SSD series [13,14].”
Method
Real-time ship detection system for wave glider based on YOLOv5s-lite-CBAM model
Lyu, Z., Wang, C., Sun, X., Zhou, Y. S., Ni, X., & Yu, P. (2024) · Applied Ocean Research · doi:10.1016/j.apor.2023.103833 · Cited by 23
Integrating AI in engineering education: A comprehensive review and student-informed module design for U.K. students
Hao, Y., Liu, Y., Liu, B., Amarantidis, G., & Ghannam, R. (2025) · IEEE Transactions on Education · doi:10.1109/te.2025.3536105 · Cited by 32
A monocular-based framework for accurate identification of spatial-temporal distribution of vehicle wheel loads under occlusion scenarios
Xu, B., Liu, X., Feng, G., & Liu, C. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.107972 · Cited by 22
Object Detection in Multispectral Remote Sensing Images Based on Cross-Modal Cross-Attention
Zhao, P., Xia, Y., & Du, Z. (2024) · Sensors · doi:10.3390/s24134098 · Cited by 24
“With the development of regression-based target detection technology, the YOLO series of algorithms has a wide range of applications [31], and the direct cross-application of the original YOLO series of algorithms with remote sensing technology will have low detection accuracy and large model size,…”
Method
Innovations in safety management for construction sites: the role of deep learning and computer vision techniques
Mohy, A. A., Bassioni, H. A., Elgendi, E. O., & Hassan, T. M. (2024) · Construction Innovation · doi:10.1108/ci-04-2023-0062 · Cited by 22
WCG-VMamba: A multi-modal classification model for corn disease
Wang, H., He, M.-F., Zhu, M., & Liu, G.-H. (2025) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2024.109835 · Cited by 24
YOLO-RD: A Road Damage Detection Method for Effective Pavement Maintenance
Wang, W., Yu, X., Jing, B., Tang, Z., Zhang, W., Wang, S., Xiao, Y., Li, S., & Yang, L. (2025) · Sensors · doi:10.3390/s25051442 · Cited by 27
“For example, the You Only Look Once (YOLO) [14] series models achieve real-time detection with their single-stage structure, making them suitable for scenarios that require quick responses.”
Privacy-Preserving Video Anomaly Detection: A Survey
Liu, Y., Liu, S., Zhu, X., Yang, H., Li, J., Guo, J., Teng, L., Yang, D., Wang, Y., & Liu, J. (2025) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2025.3600252 · Cited by 35
“This objective is balanced by hyperparameters { λ 1 , λ 2 } and formalized as follows: where C sp and C sm represent the sparsity and smoothness constraints, respectively, inspired by the infrequent and gradual nature of anomalous events.”
Anomaly detection for industrial applications: challenges, solutions, and future directions
Alzarooni, A., Iqbal, E., Khan, S. U., Javed, S., Moyo, B., & Abdulrahman, Y. (2025) · International Journal of Computer Integrated Manufacturing · doi:10.1080/0951192x.2025.2599548 · Cited by 39
“A proposed novel anomaly detection algorithm, SoftPatch (X. Jiang et al. 2022bb), looks at patch-level denoising in image samples.”
YOLO-T: Multitarget Intelligent Recognition Method for X-ray Images Based on the YOLO and Transformer Models
Wang, M., Yang, B., Wang, X., Yang, C., Xu, J., Mu, B., Xiong, K., & Li, Y. (2022) · Applied Sciences · doi:10.3390/app122211848 · Cited by 22
An Improved YOLOv5 Network for Detection of Printed Circuit Board Defects
Niu, J., Li, H., Chen, X., & Qian, K. (2023) · Journal of Sensors · doi:10.1155/2023/7270093 · Cited by 25
“feature extraction network and added some new features [24, 25].”
Background
Deep Learning for Detecting Verticillium Fungus in Olive Trees: Using YOLO in UAV Imagery
Mamalis, M. E., Kalampokis, E., Kalfas, I., & Tarabanis, K. A. (2023) · Algorithms · doi:10.3390/a16070343 · Cited by 22
“…is being actively developed, with newer and more enhanced versions coming out in the form of versions steadily throughout the last few years, with improvements made in the algorithm’s speed and performance [25] through the change in the algorithm’s architecture or the addition of new capabilities.”
Background
Wheat Powdery Mildew Detection with YOLOv8 Object Detection Model
Önler, E., & Köycü, N. D. (2024) · Applied Sciences · doi:10.3390/app14167073 · Cited by 23
Comparative Analysis of Improved YOLO v5 Models for Corrosion Detection in Coastal Environments
Yu, Q., Han, Y., Gao, X., Lin, W., & Han, Y. (2024) · Journal of Marine Science and Engineering · doi:10.3390/jmse12101754 · Cited by 23
ICA-Net: Industrial defect detection network based on convolutional attention guidance and aggregation of multiscale features
Zhao, S., Li, G., Zhou, M., & Li, M. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.107134 · Cited by 21
Lightweight Single Shot Multi-Box Detector: A fabric defect detection algorithm incorporating parallel dilated convolution and dual channel attention
Liu, S., Huang, L., Zhao, Y., & Wu, X. (2023) · Textile Research Journal · doi:10.1177/00405175231202817 · Cited by 21
“More classical network models such as YOLO, 11 SSD, 12 and Faster R-CNN 13 are widely used in various scenarios in target detection, such as UAV aerial photography, vehicle detection, and mask-wearing detection.”
Method
Hyper-Progressive Real-Time Detection Transformer (HPRT-DETR) algorithm for defect detection on metal bipolar plates
Ge, Q., Yuan, H., Zhang, Q., Hou, Y., Zang, C., Li, J., Liang, B., & Jiang, X. (2024) · International Journal of Hydrogen Energy · doi:10.1016/j.ijhydene.2024.06.028 · Cited by 21
MCTracker: Satellite video multi-object tracking considering inter-frame motion correlation and multi-scale cascaded feature enhancement
Wang, B., Sui, H., Ma, G., & Zhou, Y. (2024) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2024.06.006 · Cited by 21
A Multi-scale neighbourhood feature interaction network for photovoltaic cell defect detection
Liu, Y. C., Hua, Q., Chen, L. L., Dong, C.-R., Zhang, F., & Zhang, Y. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.112882 · Cited by 21
Efficient Deployment of Peanut Leaf Disease Detection Models on Edge AI Devices
Lv, Z., Yang, S., Ma, S., Wang, Q., Sun, J., Du, L., Han, J., Guo, Y., & Zhang, H. (2025) · Agriculture · doi:10.3390/agriculture15030332 · Cited by 21
LFD-YOLO: a lightweight fall detection network with enhanced feature extraction and fusion
Wang, H., Xu, S. H., Chen, Y., & Su, C. (2025) · Scientific Reports · doi:10.1038/s41598-025-89214-7 · Cited by 25
Real-Time Railway Obstacle Detection Based on Multitask Perception Learning
Chen, C., Qin, H., Qin, Y., & Bai, Y. (2025) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2025.3536024 · Cited by 21
“Some classic one-stage [6], [7] or two-stage [8] algorithms have been successfully applied and verified in autonomous driving, industrial inspection, etc. DL-based methods demonstrate superior adaptability in complex and dynamic environments due to their powerful feature learning and representation…”
EAD-YOLOv10: Lightweight Steel Surface Defect Detection Algorithm Research Based on YOLOv10 Improvement
Haoyan, H., Tong, J., Haibin, W., & Xin-yun, L. (2025) · IEEE Access · doi:10.1109/access.2025.3552683 · Cited by 21
“Typical algorithm models include Regions with CNN features (RCNN) and Faster-RCNN, etc. [9].”
Deep Learning-Based Medical Object Detection: A Survey
Saraei, M., Lalinia, M., & Lee, E.-J. (2025) · IEEE Access · doi:10.1109/access.2025.3553087 · Cited by 28
“In contrast, single-stage detectors, including You Only Look Once (YOLO) [7] and its modified versions [8] along with RetinaNet [9], [10], [11], integrate detection tasks into a unified framework, significantly reducing inference times while maintaining competitive accuracy [12].”
Real-time brain tumour diagnoses using a novel lightweight deep learning model
Alnageeb, M. H. O., & Supriya, M. H. (2025) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2025.110242 · Cited by 21
AC-YOLO: A lightweight ship detection model for SAR images based on YOLO11
He, R., Han, D., Shen, X., Han, B., Wu, Z., & Huang, X. (2025) · PLoS ONE · doi:10.1371/journal.pone.0327362 · Cited by 30
“One-stage ship detection algorithms primarily include the You Only Look Once (YOLO) series [30], SSD [12], and RetinaNet [31].”
An Efficient Aerial Image Detection with Variable Receptive Fields
Liu, W., Shi, L., & An, G. (2025) · Remote Sensing · doi:10.3390/rs17152672 · Cited by 27
Assessing urban emergency medical services accessibility for older adults considering ambulance trafficability using a deep learning approach
Liu, Z., Zhang, E., Pan, S., Li, S., Long, Y., & Witlox, F. (2025) · Sustainable Cities and Society · doi:10.1016/j.scs.2025.106804 · Cited by 21
Deep learning for sustainable agriculture: automating rice and paddy ripeness classification for enhanced food security
Eliwa, E. H. I., & El‐Hafeez, T. A. (2025) · Egyptian Informatics Journal · doi:10.1016/j.eij.2025.100785 · Cited by 21
Development and evolution of YOLO in object detection: A survey
Tian, Y., Xu, W., Yang, B., Yang, X., Guo, H., Wang, G., & Yu, H. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.132436 · Cited by 33
Multi-Target Association for UAVs Based on Triangular Topological Sequence
Li, X., Wu, L., Niu, Y., & Ma, A. (2022) · Drones · doi:10.3390/drones6050119 · Cited by 21
A Deep Learning Approach to Classify Drones and Birds
Selvi, S. S., Pavithraa, S., Raghavan, D., & Chaitra, E. (2022) · IEEE Mysore Sub Section International Conference (MysuruCon) · doi:10.1109/mysurucon55714.2022.9972589 · Cited by 21
“The neck layers based on FPN collect feature maps from various stages and are composed of many bottom-up and top-down paths [9] and each level of pyramid detects objects at different scales.”
Background
RT-Droid: a novel approach for real-time android application analysis with transfer learning-based CNN models
Taşyürek, M., & Arslan, R. S. (2023) · Journal of Real-Time Image Processing · doi:10.1007/s11554-023-01311-w · Cited by 21
Citrus yield prediction using deep learning techniques: A combination of field and satellite data
Moussaid, A., Fkihi, S. E., Zennayi, Y., Kassou, I., Bourzeix, F., Lahlou, O., Mansouri, L. E., & Imani, Y. (2023) · Journal of Open Innovation Technology Market and Complexity · doi:10.1016/j.joitmc.2023.100075 · Cited by 23
Review of deep learning approaches in solving rock fragmentation problems
Ronkin, M., Akimova, E. N., & Misilov, V. E. (2023) · AIMS Mathematics · doi:10.3934/math.20231219 · Cited by 22
“However, today most of these architectures can be modified in the feature extractor part for different goals [75,76].”
Background
Radar sensor based machine learning approach for precise vehicle position estimation
Sohail, M. A., Khan, A. U., Sandhu, M. M., Shoukat, I. A., Jafri, M. R., & Shin, H. (2023) · Scientific Reports · doi:10.1038/s41598-023-40961-5 · Cited by 23
“Real-time data is generated against these simulation scenarios for radar sensors, which is used for the calculation of precision and IOU value using the YOLO algorithm 35 .”
Method
Exploring the efficacy and comparative analysis of one-stage object detectors for computer vision: a review
Mustapha, A. A., & Yoosuf, M. S. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-17751-2 · Cited by 28
“In order to forecast the bounding box and class label for the objects in each grid cell, YOLO [5] splits the image into a grid and assigns a detection head to each grid cell.”
Interactive Design With Gesture and Voice Recognition in Virtual Teaching Environments
Fang, K., & Wang, J. (2024) · IEEE Access · doi:10.1109/access.2023.3348846 · Cited by 21
“Subsequently, an improved annotation scheme inspired by the YOLO algorithm [43] was adopted, incorporating existence and category labels (Existence, C1, C2, C3), resulting in an output dimension of (1375,4).”
Method
PRogramAR: Augmented Reality End-User Robot Programming
Ikeda, B., & Szafir, D. J. (2024) · ACM Transactions on Human-Robot Interaction · doi:10.1145/3640008 · Cited by 21
Autonomous control of UAV trajectory based on RHC-Radau method in complex penetration combat environment
Yang, Z. C., Yuan, Z., Wang, X., Huang, J., & Zhou, D. (2024) · Aerospace Science and Technology · doi:10.1016/j.ast.2024.108915 · Cited by 21
Deep Visual-guided and Deep Reinforcement Learning Algorithm Based for Multip-Peg-in-Hole Assembly Task of Power Distribution Live-line Operation Robot
Zheng, L., Ai, J., Wang, Y., Tang, X., Wu, S., Cheng, S., Guo, R., & Dong, E. (2024) · Journal of Intelligent & Robotic Systems · doi:10.1007/s10846-024-02079-2 · Cited by 21
“In contrast, regression-based algorithms, such as the YOLO series [37–39], are end-to-end processes that can directly regress the object’s classiﬁcation and position without the”
Evaluation of Spectrum-Aided Visual Enhancer (SAVE) in Esophageal Cancer Detection Using YOLO Frameworks
Chou, C., Karmakar, R., Tsao, Y.-M., Jie, L. W., Mukundan, A., Huang, C., Chen, T., Ko, C.-Y., & Wang, H. (2024) · Diagnostics · doi:10.3390/diagnostics14111129 · Cited by 21
“Its architectural design revolves around three pivotal components: the Model Backbone, Model Neck, and Model Head [44,45].”
Background
Design and Augmentation of a Deep Learning Based Vehicle Detection Model for Low Light Intensity Conditions
Vishwakarma, P. K., & Jain, N. (2024) · SN Computer Science · doi:10.1007/s42979-024-02944-9 · Cited by 21
“Considered IOU thresholds range from fifty to ninety-five [28].”
A Comprehensive Systematic Review of YOLO for Medical Object Detection (2018 to 2023)Preprint
Qureshi, R., Ragab, M. G., Abdulkadir, S. J., Muneer, A., ALQUSHAIB, A., Sumiea, E. H., Alhussian, H., & Al-Selwi, S. M. (2023) · doi:10.36227/techrxiv.23681679.v1 · Cited by 24
Identification of Rare Wildlife in the Field Environment Based on the Improved YOLOv5 Model
Su, X., Zhang, J., Ma, Z., Dong, Y., Zi, J., Xu, N., Zhang, H., Xu, F., & Chen, F. (2024) · Remote Sensing · doi:10.3390/rs16091535 · Cited by 21
“Secondly, YOLO thoroughly analyzes the image when making predictions and possesses a strong generalization ability, making it superior to R-CNN [28].”
BEW-YOLOv8: A deep learning model for multi-scene and multi-scale flood depth estimation
Liu, B., Li, Y., Feng, X., & Lian, P. (2024) · Journal of Hydrology · doi:10.1016/j.jhydrol.2024.132139 · Cited by 24
A multimodal framework for pepper diseases and pests detection
Liu, J., & Wang, X. (2024) · Scientific Reports · doi:10.1038/s41598-024-80675-w · Cited by 20
Deep learning in produce perception of harvesting robots: A comprehensive review
Jin, Y., Xia, X., Gao, Q., Yue, Y. G., Lim, E. G., Wong, P. W. H., Ding, W., & Zhu, X. (2025) · Applied Soft Computing · doi:10.1016/j.asoc.2025.112971 · Cited by 22
AI explainability in oculomics: How it works, its role in establishing trust, and what still needs to be addressed
An, S., Teo, K. Y. C., McConnell, M. V., Marshall, J., Galloway, C., & Squirrell, D. (2025) · Progress in Retinal and Eye Research · doi:10.1016/j.preteyeres.2025.101352 · Cited by 27
Swin Transformer-Based Object Detection Model Using Explainable Meta-Learning Mining
Baek, J.-W., & Chung, K. (2023) · Applied Sciences · doi:10.3390/app13053213 · Cited by 20
An Active Multi-Object Ultrafast Tracking System with CNN-Based Hybrid Object Detection
Li, Q., Hu, S., Shimasaki, K., & Ishii, I. (2023) · Sensors · doi:10.3390/s23084150 · Cited by 20
“YOLO was the earliest one-stage target detection algorithm applied to actual scenes, obtaining stable and high-speed detection results [52].”
Background
A Symbol Recognition System for Single-Line Diagrams Developed Using a Deep-Learning Approach
Bhanbhro, H., Hooi, Y. K., Kusakunniran, W., & Amur, Z. H. (2023) · Applied Sciences · doi:10.3390/app13158816 · Cited by 26
Automatic Solder Defect Detection in Electronic Components Using Transformer Architecture
Liu, Y., & Wu, H. (2023) · IEEE Transactions on Components Packaging and Manufacturing Technology · doi:10.1109/tcpmt.2023.3344096 · Cited by 20
“Since the introduction of U-Net [3], convolutional neural networks (CNNs) and their derivatives, including Faster-RCNN [4], Mask-RCNN [5], and YOLOs [6], [7], have gained significant attention and have been widely employed in the field of industrial defect detection [8], [9], [10].”
Method
A novel anti-loosening bolt looseness diagnosis of bolt connections using a vision-based technique
Luo, J., Li, K., Xie, C., Yan, Z., Li, F., Jia, X., & Wang, Y. (2024) · Scientific Reports · doi:10.1038/s41598-024-62560-8 · Cited by 20
“Yang et al. 16 comparatively tested the detection performance of three YOLO target detection algorithms 28 for bolt loosening identification, namely, YOLO v3, YOLO v4 and YOLO v4-Tiny.”
Method
A user‐friendly method to get automated pollen analysis from environmental samples
Gimenez, B., Joannin, S., Pasquet, J., Beaufort, L., Gally, Y., de Garidel‐Thoron, T., Combourieu‐Nebout, N., Bouby, L., Canal, S., Ivorra, S., et al. (2024) · New Phytologist · doi:10.1111/nph.19857 · Cited by 22
“…speed and accuracy, on almost any object, for example molds in fruits (Jubayer et al ., 2021), arctic benthic fauna (Marini et al ., 2022), Plasmodium falciparum (Zedda et al ., 2022), and even on complex images with dense and heterogeneous backgrounds (Diwan et al ., 2022; Jiang et al ., 2022).”
Background
A review of the development of YOLO object detection algorithm
Liang, J. (2024) · Applied and Computational Engineering · doi:10.54254/2755-2721/71/20241642 · Cited by 25
Three-dimensional reconstruction and damage localization of bridge undersides based on close-range photography using UAV
Jiang, S., Zhang, Y., Wang, F., & Xu, Y. (2024) · Measurement Science and Technology · doi:10.1088/1361-6501/ad90fb · Cited by 19
“In response to this demand, the YOLO series models, widely applied in the industry due to their fast speed and high accuracy, can be utilized for bridge object segmentation in this section [30].”
Estimating optimal crop-load for individual branches in apple tree canopies using YOLOv8
Ahmed, D., Sapkota, R., Churuvija, M., & Karkee, M. (2024) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2024.109697 · Cited by 46
SCCA-YOLO: A Spatial and Channel Collaborative Attention Enhanced YOLO Network for Highway Autonomous Driving Perception System
Wei, F., & Wang, W. (2025) · Scientific Reports · doi:10.1038/s41598-025-90743-4 · Cited by 22

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

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

A Review of Yolo Algorithm Developments

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Citations

Cites

Cited by

Related

Metadata

Record

About these checks