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

作     者：Licheng Sun Heping Li Liang Wang 

作者机构：College of Information Science and TechnologyBeijing University of TechnologyBeijing100124China Chinese Institute of Coal ScienceBeijing100013China State Key Laboratory for Intelligent Coal Mining and Strata ControlBeijing100013China Engineering Research Center of Digital Community of Ministry of EducationBeijing100124China 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2024年第80卷第9期

页      面：4543-4560页

核心收录：

学科分类：08[工学] 080203[工学-机械设计及理论] 0802[工学-机械工程] 

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) 

主　　题：Object detection deep learning safety helmet wearing detection feature extraction attention mechanism 

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 *** 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 ***,a new safety helmet-wearing detection method based on deep learning is ***,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 ***,a new detection block combining the dilate convolution and attention mechanism is proposed and introduced into the prediction *** block can effectively extract deep featureswhile retaining information on fine-grained details,such as edges and small ***,some newly emerged modules are incorporated into the proposed network to improve safety helmetwearing detection performance *** experiments on open dataset validate the proposed *** reaches better performance on helmet-wearing detection and even outperforms the state-of-the-art *** 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 *** 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.