Ziwei Tian
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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