A Review of Yolo Algorithm Developments

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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