湖南大学学报（自然科学版）2023，Vol.50Issue(12)：28-38,11.DOI:10.16339/j.cnki.hdxbzkb.2023297
基于改进YOLOv5的轻量化无人机检测算法
Lightweight UAV Detection Algorithm Based on Improved YOLOv5
摘要
Abstract
Aiming at the problem that the existing UAV detection algorithms cannot simultaneously take into account detection speed and accuracy,a lightweight UAV detection algorithm,i.e.,Tiny Drone Real-time Detection-YOLO(TDRD-YOLO)based on YOLOv5s,is proposed in this paper.Firstly,the multi-scale fusion layer and output detection layer of YOLOv5s are used as the neck network and head network,respectively.MobileNetv3 lightweight network is introduced to reconstruct the original backbone network,and the channel behind the backbone network is compressed on the basis of the original YOLOv5s to reduce the size of the network model.Secondly,the attention mechanism of the Bneck module in the backbone network is modified from SE to CBAM(Convolutional Block Attention Module),and the CBAM is introduced in the neck network to make the network model pay more attention to the target features.Finally,the activation function of the neck network is modified as h-swish to further improve the accuracy of the model.Experimental results show that the average detection accuracy of the TDRD-YOLO algorithm proposed reaches 96.8％.Compared with YOLOv5s,the number of parameters is reduced by 11 times,the detection speed increases by 1.5 times,and the model size is reduced by 8.5 times.Experiments show that the proposed algorithm can greatly reduce the model size and improve the detection speed while maintaining good detection performance.关键词
无人机检测/YOLOv5/轻量化/注意力机制/深度学习Key words
UAV detection/YOLOv5/lightweight/attention mechanism/deep learning分类
信息技术与安全科学引用本文复制引用
彭艺,涂馨月,杨青青,李睿..基于改进YOLOv5的轻量化无人机检测算法[J].湖南大学学报（自然科学版）,2023,50(12):28-38,11.基金项目
国家自然科学基金资助项目(61761025),National Natural Science Foundation of China(61761025) （61761025）
云南省计算机技术应用重点实验室开放基金资助项目(2021102),Development Fund of Key Laboratory of Computer Technology Application in Yunnan Province(2021102) （2021102）
