Improved YOLOv5s UAV View Small Target Detection Algorithm

被引：0
|
作者：
Liu, Tao [1 ,2 ]
Gao, Yimeng [1 ]
Chai, Rui [1 ]
Li, Zhengtong [1 ]
机构：
[1] School of Software, Liaoning Technical University, Liaoning, Huludao,125105, China
[2] Department of Basic Teaching, Liaoning Technical University, Liaoning, Huludao,125105, China
来源：
Computer Engineering and Applications | 2024年 / 60卷 / 01期
关键词：
The small target image from the UAV perspective has the characteristics of dense target distribution; unbalanced category and inconspicuous features; which leads to the problem of missed detection and false detection in the target detection task. To solve these problems; an improved YOLOv5s small target detection method is proposed to improve the accuracy and accuracy of target detection. First; it reclusters the anchor box to lock the detection area more accurately. Secondly; the backbone network structure is changed and convolution is added to the spatial pyramid pool layer to ensure that the detection target features are fully obtained. At the same time; the C3 module in the network structure is replaced with a lightweight SEC2f module that fuses the channel attention mechanism to improve the local feature acquisition ability of the network for small target detection. Finally; the features of the target area are extracted effectively by combining the decoupled detection head with the adaptive anchor frame calculation. Under the same parameters and environmental conditions; the detection accuracy on DOTA data set and VisDrone data set is improved by 6.1% and 5.2%; respectively; indicating the effectiveness of the improved method on small target detection tasks. The comparison experiment on voc2007+2012 public data set shows the universality of the improved algorithm. © 2024 Journal of Computer Engineering and Applications Beijing Co; Ltd; Science Press. All rights reserved;
D O I：
10.3778/j.issn.1002-8331.2304-0150
中图分类号：
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页码：110 / 121
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