HWD-YOLO:A New Vision-Based Helmet Wearing Detection Method  

作　　者：Licheng Sun Heping Li Liang Wang 

机构地区：[1]College of Information Science and Technology,Beijing University of Technology,Beijing,100124,China [2]Chinese Institute of Coal Science,Beijing,100013,China [3]State Key Laboratory for Intelligent Coal Mining and Strata Control,Beijing,100013,China [4]Engineering Research Center of Digital Community of Ministry of Education,Beijing,100124,China

出　　处：《Computers, Materials & Continua》2024年第9期4543-4560,共18页计算机、材料和连续体（英文）

基　　金：supported in part by National Natural Science Foundation of China under Grant No.61772050;,Beijing Municipal Natural Science Foundation under Grant No.4242053;Key Project of Science and Technology Innovation and Entrepreneurship of TDTEC(No.2022-TD-ZD004).

摘　　要：It is crucial to ensure workers wear safety helmets when working at a workplace with a high risk of safety accidents,such as construction sites and mine tunnels.Although existing methods can achieve helmet detection in images,their accuracy and speed still need improvements since complex,cluttered,and large-scale scenes of real workplaces cause server occlusion,illumination change,scale variation,and perspective distortion.So,a new safety helmet-wearing detection method based on deep learning is proposed.Firstly,a new multi-scale contextual aggregation module is proposed to aggregate multi-scale feature information globally and highlight the details of concerned objects in the backbone part of the deep neural network.Secondly,a new detection block combining the dilate convolution and attention mechanism is proposed and introduced into the prediction part.This block can effectively extract deep featureswhile retaining information on fine-grained details,such as edges and small objects.Moreover,some newly emerged modules are incorporated into the proposed network to improve safety helmetwearing detection performance further.Extensive experiments on open dataset validate the proposed method.It reaches better performance on helmet-wearing detection and even outperforms the state-of-the-art method.To be more specific,the mAP increases by 3.4%,and the speed increases from17 to 33 fps in comparison with the baseline,You Only Look Once(YOLO)version 5X,and themean average precision increases by 1.0%and the speed increases by 7 fps in comparison with the YOLO version 7.The generalization ability and portability experiment results show that the proposed improvements could serve as a springboard for deep neural network design to improve object detection performance in complex scenarios.

关 键 词：Object detection deep learning safety helmet wearing detection feature extraction attention mechanism 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]