YOLO系列目标检测算法研究进展  

Research Progress of YOLO Series Target Detection Algorithms

作　　者：王琳毅 白静[1,2] 李文静 蒋金哲 WANG Linyi;BAI Jing;LI Wenjing;JIANG Jinzhe(School of Computer Science and Engineering,North Minzu University,Yinchuan 750021,China;The Key Laboratory of Images&Graphics Intelligent Processing of State Ethnic Affairs Commission,North Minzu University,Yinchuan 750021,China)

机构地区：[1]北方民族大学计算机科学与工程学院,银川750021 [2]国家民委图形图像智能处理实验室,银川750021

出　　处：《计算机工程与应用》2023年第14期15-29,共15页Computer Engineering and Applications

基　　金：国家自然科学基金(62162001,61762003);宁夏自然科学基金(2022AAC02041);宁夏优秀人才支持计划;北方民族大学创新项目(YCX22181)。

摘　　要：YOLO算法是目标检测中研究的热点方向之一。近几年,随着YOLO系列算法及其改进模型的不断提出,使其在目标检测领域取得了优异的成绩,被广泛应用于现实中各个领域。针对YOLO系列目标检测算法,整理了目标检测典型数据集及评价指标;回顾了YOLO整体框架以及YOLOv1~YOLOv7目标检测算法的发展历程;总结了在输入、特征提取和预测这三个阶段下的数据增强、轻量化网络构建和IOU损失优化等八个改进方向的模型及性能;介绍了YOLO算法应用领域;结合目标检测目前存在的实际问题,总结并展望了YOLO算法的发展方向。The YOLO-based algorithm is one of the hot research directions in target detection.In recent years,with the continuous proposition of YOLO series algorithms and their improved models,the YOLO-based algorithm has achieved excellent results in the field of target detection and has been widely used in various fields in reality.This article first introduces the typical datasets and evaluation index for target detection and reviews the overall YOLO framework and the development of the target detection algorithm of YOLOv1~YOLOv7.Then,models and their performance are summarized across eight improvement directions,such as data augmentation,lightweight network construction,and IOU loss optimization,at the three stages of input,feature extraction,and prediction.Afterwards,the application fields of YOLO algorithm are introduced.Finally,combined with the actual problems of target detection,it summarizes and prospects the development direction of the YOLO-based algorithm.

关 键 词：计算机视觉 目标检测 YOLO 改进模型 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]