A Method on Mask Wearing Detection of Natural Population Based on Improved YOLOv4

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A Method on Mask Wearing Detection of Natural Population Based on Improved YOLOv4

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[1]XUE Junxiao,WU Xuecheng,WANG Shihao,et al.A Method on Mask Wearing Detection of Natural Population Based on Improved YOLOv4[J].Journal of Zhengzhou University (Engineering Science),2022,43(04):16-22.[doi:10.13705/j.issn.1671-6833.2022.04.020]

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Journal of Zhengzhou University (Engineering Science)[ISSN 1671-6833/CN 41-1339/T] Volume: 43卷 Number of periods: 2022 04 Page number: 16-22 Column: Public date: 2022-07-03

Title:: A Method on Mask Wearing Detection of Natural Population Based on Improved YOLOv4

Author(s):: XUE Junxiao; WU Xuecheng; WANG Shihao; TIAN Mengmeng; SHI Lei; School of Cyber Science and Engineering,Zhengzhou University,Zhengzhou 450002,China

Keywords:: deep learning; mask wearing detection; YOLOv4; coordinate attention mechanism; neural network

CLC:: TP39

DOI:: 10.13705/j.issn.1671-6833.2022.04.020

Abstract:: The mask wearing detection in natural scenes is often affected by various factors such as the style and color of the mask,the skin color of the wearer,and the weather.In this study,based on the original YOLOv4,the coordinate attention mechanism was introduced to improve the utilization of the backbone network for spatial information of shallow feature maps and better capture small objects-masks.At the same time,it could enrich the semantic information of shallow feature maps and strengthen the long-distance dependencies to more accurately locate and identify object regions.This paper improved the network structure of YOLOv4 to enhance the capacity and depth of the overall network,so as to expand the receptive fields and improved the robustness of the algorithm.The introduction of DIoU-NMS could alleviate the phenomenon that the object was blocked and incorrectly suppressed.DIoU-NMS could perform NMS from the two aspects of IoU and center point distance of bounding boxes,so that the selection of the IoU threshold was not so harsh.The experimental results showed that the average precision of the improved YOLOv4 was 95.81%,which was 4.62% higher than the average precision of the original YOLOv4.The improved YOLOv4 had exciting performance and could complete the task of comprehensive and accurate mask wearing detection in natural scenarios.

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Last Update: 2022-07-03