Small Target HelmetWearing Detection Algorithm Based on Improved YOLO V5  

作　　者：Jiajing Hu Junqiu Li Qinghui Zhang 

机构地区：[1]Southwest Forestry University,Kunming 650224,China

出　　处：《国际计算机前沿大会会议论文集》2023年第1期60-77,共18页International Conference of Pioneering Computer Scientists, Engineers and Educators（ICPCSEE）

摘　　要：To solve problems such as the low detection accuracy of helmet wear-ing,missing detection and poor real-time performance of embedded equipment in the scene of remote and small targets at the construction site,the text proposes an improved YOLO v5 for small target helmet wearing detection.Based on YOLO v5,the self-attention transformer mechanism and swin transformer module are introduced in the feature fusion step to increase the receptivefield of the con-volution kernel and globally model the high-level semantic feature information extracted from the backbone network to make the model more focused on hel-met feature learning.Replace some convolution operators with lighter and more efficient Involution operators to reduce the number of parameters.The connection mode of the Concat is improved,and 1×1 convolution is added.The experimental results compared with YOLO v5 show that the size of the improved helmet detec-tion model is reduced by 17.8%occupying only 33.2 MB,FPS increased by 5%,and mAP@0.5 reached 94.9%.This approach effectively improves the accuracy of small target helmet wear detection,and meets the deployment requirements for low computational power embedded devices.

关 键 词：Helmet wearing detection YOLO V5 Small object detection TRANSFORMER Swin Transformer INVOLUTION 

分 类 号：O17[理学—数学]