数据与计算发展前沿2023，Vol.5Issue(6)：161-172,12.DOI:10.11871/jfdc.issn.2096-742X.2023.06.015
改进的轻量级YOLOv5算法在行人检测的应用
Application of Improved Lightweight YOLOv5 Algorithm in Pedestrian Detection
摘要
Abstract
[Objective]In this paper,we propose an improved YOLOv5 algorithm to address the problems of the high computational complexity of pedestrian detection algorithms,low detection accuracy,and slow detection speed,which can be better applied to pedestrian detection.[Methods]Firstly,the vanilla convolution in the YOLOv5 backbone network is replaced by the depthwise separable convolution,which reduces the number of calculations and parameters while improving detection accuracy.Then,channel attention and spatial attention are incorporated into the feature fusion part of the backbone network,which can force our network to focus on the location and channel information of pedestrians in the image.Finally,the EIOU loss function is used to optimize the proposed model,and the K-means++ clustering algorithm is used to generate priori boxes.[Results]The results show our proposed model can achieve a detection accuracy of 89%,which is 7.6%higher than the original backbone,and the detection speed reaches 106 frames per second when using the INRIA pedestrian detection dataset.[Conclusions]Our proposed method significantly improves the speed and accuracy of pedestrian detection,has also small parameters and is easier to detect and deploy in real-time.关键词
行人检测/深度学习/YOLOv5/深度可分离卷积/注意力机制Key words
pedestrian detection/deep learning/YOLOv5/deep separable convolution/attention mechanism引用本文复制引用
王子元,王国中..改进的轻量级YOLOv5算法在行人检测的应用[J].数据与计算发展前沿,2023,5(6):161-172,12.基金项目
国家重点研发计划"宽带通信和新型网络"(2019YFB1802702) （2019YFB1802702）
