计算机工程与应用2024，Vol.60Issue(1)：110-121,12.DOI:10.3778/j.issn.1002-8331.2304-0150
改进YOLOv5s的无人机视角下小目标检测算法
Improved YOLOv5s UAV View Small Target Detection Algorithm
摘要
Abstract
The small target image from the UAV perspective has the characteristics of dense target distribution,unbal-anced 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 param-eters 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 compari-son experiment on voc2007+2012 public data set shows the universality of the improved algorithm.关键词
YOLOv5s/聚类算法/SEC2f模块/空间金字塔池化/解耦检测头Key words
YOLOv5s/clustering algorithm/SEC2f module/spatial pyramid pool/decoupling detection head分类
信息技术与安全科学引用本文复制引用
刘涛,高一萌,柴蕊,李政通..改进YOLOv5s的无人机视角下小目标检测算法[J].计算机工程与应用,2024,60(1):110-121,12.基金项目
国家自然科学基金(52174183). （52174183）
