计算机科学与探索2024，Vol.18Issue(9)：2221-2238,18.DOI:10.3778/j.issn.1673-9418.2402044
YOLO系列目标检测算法综述
Survey of Development of YOLO Object Detection Algorithms
摘要
Abstract
In recent years,deep learning-based object detection algorithms have been a hot topic in computer vision research,with the YOLO(you only look once)algorithm standing out as an excellent object detection algorithm.The evolution of its network architecture has played a crucial role in improving detection speed and accuracy.This paper conducts a comprehensive horizontal analysis of the overall frameworks of YOLOv1 to YOLOv9,comparing the network architecture(backbone network,neck layers and head layers)and loss functions.The strengths and limi-tations of different improvement methods are thoroughly discussed,with a specific evaluation of the impact of these improvements on model accuracy.This paper also delves into discussions on dataset selection and construction methods,the rationale behind choosing different evaluation metrics,and their applicability and limitations in various application scenarios.It further explores specific improvement methods for YOLO algorithm in five application do-mains(industrial,transportation,remote sensing,agriculture,biology),and discusses the balance among detection speed,accuracy,and complexity in these application domains.Finally,this paper analyzes the current development status of YOLO in various fields,summarizes existing issues in YOLO algorithm research through specific exam-ples,and in conjunction with the trends in application domains,provides an outlook on the future of the YOLO algo-rithm.It also offers detailed explanations for four future research directions of YOLO(multi-task learning,edge computing,multimodal integration,virtual and augmented reality technology).关键词
YOLO算法/目标检测/计算机视觉/特征提取/卷积神经网络Key words
YOLO algorithm/object detection/computer vision/feature extraction/convolutional neural network分类
信息技术与安全科学引用本文复制引用
徐彦威,李军,董元方,张小利..YOLO系列目标检测算法综述[J].计算机科学与探索,2024,18(9):2221-2238,18.基金项目
国家自然科学基金(61801190). This work was supported by the National Natural Science Foundation of China(61801190). （61801190）
