Lightweight helmet wearing detection algorithm based on improved YOLOv5s
LI Guan
LI Zhiwei
CHEN Hao
TONG Bo
ZHANG Xianyang
Abstract：A lightweight target detection algorithm based on improved YOLOv5s is proposed to address the dif-ficulty of deploying embedded devices for the neural network-based helmet detection work scenario model.Firstly,the backbone network of YOLOv5s is replaced by a lightweight network,MobilenetV3,to reduce the number of parameters and computation of the model,and the SPPF module of the model is retained to improve the model's ability to detect targets of different sizes;secondly,an attention mechanism is added between the neck and head of the model to better capture the target information in the image and to improve the accuracy and robustness;finally,replace the model loss function with EIoU to accelerate the model convergence and im-prove the model detection accuracy.The proposed model is trained and validated by the self-constructed hel-met dataset,and the experimental results show that compared with the original model,the detection accuracy is improved by 1.2%,the number of parameters is reduced by 39.4%,the computation amount is reduced by 56.3%,and the model volume is compressed by 38.6%,which provides an effective method for the deployment of helmet recognition algorithms based on the improved YOLOv5s on embedded devices.
Keywords：YOLOv5lightweightingattention mechanismloss functionMobilenetV3
Publication Date：2024-06-28
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：10( 32-41 )