基于Mask-YOLO的复杂场景口罩佩戴检测

doi:10.3969/j.issn.0255-8297.2022.01.009

Abstract

Abstract: Aiming at the problem of low detection accuracy caused by occlusion, density and small scale in mask wearing detection in public places, a Mask-YOLO algorithm is proposed based on real-time target detection algorithm YOLOv3. First, the algorithm introduces channel attention mechanism in the process of feature fusion, effectively highlights the important features, reduces the influence of redundant features after fusion, and effectively improves the feature utilization. Then, complete intersection over union (CIoU) loss is used instead of mean square error (MSE) as the loss function of frame regression to improve the positioning accuracy. Finally, in addition to the cases of detecting wearing and not wearing masks, incorrect wearing of masks is also detected. Experimental results show that Mask-YOLO algorithm improves mean average precision (mAP) by 4.78% when frame per second (FPS) decreases by only 1% compared with YOLOv3 algorithm. As compared with other mainstream target detection algorithms, Mask-YOLO algorithm also has better detection effect and robustness for mask wearing detection in complex scenes.

Key words: mask wearing detection, Mask-YOLO, attention mechanism, feature fusion, loss function

CLC Number:

TP391.41

WEI Mingjun, ZHOU Taiyu, JI Zhanlin, ZHANG Xinnan. Mask Wearing Detection in Complex Scenes Based on Mask-YOLO[J]. Journal of Applied Sciences, 2022, 40(1): 93-104.

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Mask Wearing Detection in Complex Scenes Based on Mask-YOLO

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