Mask Wearing Detection in Dim Lighting Conditions based on Improved YOLOv5
Authors: 
Ziwei Tian, Jie Huang, Yang Yang, Weiying NieAuthors Info & Claims
Pages 49 - 55
Abstract
The COVID-19 epidemic is still rampant around the world. Wearing masks can effectively block the spread of novel coronavirus, while mask wearing detection can timely remind people in public places to wear masks. In order to solve the problem of low accuracy of mask wearing detection under under dim lighting conditions, an improved YOLOv5 algorithm is proposed. Firstly, the Low Light Intensity Image Enhancement module (LLIIE) was embedded into the original YOLOv5 algorithm to improve the algorithm's night vision ability; Secondly, we added the Convolutional Block Attention Module (CBAM) to enhance feature extraction ability; The CIoU loss function is used to replace the original loss function, which makes the detected target position more accurate. The improved YOLOv5 algorithm is trained and tested on the self-built dataset, and its mean Average Precision, Accuracy and Recall reach 87.1%, 89.3% and 82.7% respectively. The experimental results show that the improved YOLOv5 algorithm has the best detection performance and higher practical value compared with other excellent algorithms in dim light condition.
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Index Terms
- Mask Wearing Detection in Dim Lighting Conditions based on Improved YOLOv5
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Published In
March 2023
316 pages
ISBN:9798400700002
DOI:10.1145/3592307
Copyright © 2023 ACM.
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Association for Computing Machinery
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Publication History
Published: 14 August 2023
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ICECC 2023
ICECC 2023: 2023 The 6th International Conference on Electronics, Communications and Control Engineering
March 24 - 26, 2023
Fukuoka, Japan
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