计算机与现代化Issue(8)：108-113,6.DOI:10.3969/j.issn.1006-2475.2024.08.017
基于YOLOv5s的无人机图像车辆检测
Vehicle Detection in UAV Image Based on YOLOv5s
摘要
Abstract
The problem of complex backgrounds and large variations in target scales in vehicle images captured by unmanned aerial vehicle(UAV)makes it difficult for existing neural network models to detect small target objects when performing vehicle detection,which can easily lead to false detection and missed detection of small target objects.To solve this problem,an improv-eed method based on the YOLOv5s neural network is proposed.Firstly,we use the K-means++algorithm to cluster dataset to ob-tain better anchor.Secondly,the SPD-Conv small target detection module is combined to reduce the false detection and miss de-tection rate,so as to improve the accuracy of vehicle detection.Finally,the detection head module is replaced by a decoupled head module to decouple the classification and regression tasks,thus further improve the classification accuracy.The article uses VisDrone-2019-DET dataset for vehicle detection,the mean average precision(mAP)of the improved network in this paper reaches 53.0%,which is 6.3 percentage points higher than the original YOLOv5s model,and can effectively reduce the probabil-ity of false detection and missed detection of small objects,enable more accurate vehicle detection.关键词
YOLOv5s/小目标/车辆检测/K-means++/SPD-Conv/检测头解耦模块Key words
YOLOv5s/small object/vehicle detection/K-means++/SPD-Conv/decoupled head model分类
信息技术与安全科学引用本文复制引用
王涛,黄丹,刘禅奕,朱桃..基于YOLOv5s的无人机图像车辆检测[J].计算机与现代化,2024,(8):108-113,6.基金项目
四川省科技厅省院省校科技合作项目(2022YFSY0056) （2022YFSY0056）
人工智能四川省重点实验室开放基金项目(019RYJ07) （019RYJ07）
