Research Progress of YOLO Series Target Detection Algorithms

被引:0
作者
Wang, Linyi [1 ]
Bai, Jing [1 ,2 ]
Li, Wenjing [1 ]
Jiang, Jinzhe [1 ]
机构
[1] School of Computer Science and Engineering, North Minzu University, Yinchuan
[2] The Key Laboratory of Images, Graphics Intelligent Processing of State Ethnic Affairs Commission, North Minzu University, Yinchuan
关键词
computer vision; improved model; object detection; YOLO;
D O I
10.3778/j.issn.1002-8331.2301-0081
中图分类号
学科分类号
摘要
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. © 2023 Journal of Computer Engineering and Applications Beijing Co., Ltd.; Science Press. All rights reserved.
引用
收藏
页码:15 / 29
页数:14
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