广东工业大学学报2024，Vol.41Issue(3)：71-80,10.DOI:10.12052/gdutxb.230044
基于YOLOv5的轻量化无人机航拍小目标检测算法
Small Target Detection Algorithm for Lightweight UAV Aerial Photography Based on YOLOv5
摘要
Abstract
A lightweight unmanned aerial vehicle(UAV)aerial photography small target detection algorithm GA-YOLO based on YOLOv5 is proposed to address the problem of small target feature size,complex background,and dense distribution in images from the perspective of UAV aerial photography.This algorithm improves the Mosaic data augmentation method and overall network structure,and adds a small object detection head.At the same time,a lightweight global attention module and a parallel spatial channel attention mechanism module are designed to enhance the network's global feature extraction ability and the competition and cooperation between convolutional channels during the training process.Based on the 4.0 version of YOLOv5s,experiments were conducted on the publicly available drone aerial photography dataset VisDrone2019-DET.The results showed that the improved model reduced the number of parameters by 48%and the computational complexity by 26%compared to the original model,and mAP@0.5 improved by 4.9 percentage points,mAP@0.50.95 increased by 3.3 percentage points,effectively enhancing the detection capability of unmanned aerial vehicles for dense small targets from an aerial perspective.关键词
无人机航拍/YOLOv5s/小目标检测/数据增强/注意力机制Key words
UAV aerial photography/YOLOv5s/small target detection/data enhancement/attention mechanism分类
信息技术与安全科学引用本文复制引用
李雪森,谭北海,余荣,薛先斌..基于YOLOv5的轻量化无人机航拍小目标检测算法[J].广东工业大学学报,2024,41(3):71-80,10.基金项目
国家自然科学基金资助项目(61971148) （61971148）
国家自然科学基金资助项目(U22A2054) （U22A2054）
广东省基础与应用基础研究基金联合基金重点项目(2019B1515120036) （2019B1515120036）
广西自然科学基金重点项目(2018GXNSFDA281013) （2018GXNSFDA281013）
