基于YOLOv3的公共场所口罩佩戴检测方法

doi:10.16088/j.issn.1001-6600.2022030402

Abstract

Abstract: Aiming at the problem of low detection accuracy due to the large number of small-scale targets in the detection of masks worn by people in public places, the feature pyramid structure of YOLOv3 is improved by the method in this paper. Firstly, the rich location information of the low-level feature map is transmitted to the middle-level and high-level feature map by using the jump connection and the location feature enhancement module LFE containing channel attention, so as to strengthen the recognition of small targets. Secondly, the bounding box regression using the CIoU loss function improves the positioning accuracy of the algorithm. In addition, nonstandard wearing of masks is also detected. The experimental results show that on the self-made mask wearing dataset, the mAP of the improved YOLOv3 algorithm reaches 86.96%, which is 3.30% higher than that of the YOLOv3 algorithm. The result is also better than those of the mainstream algorithms such as Faster R-CNN, SSD300, DSSD321 and YOLOv4. The detection speed FPS of the algorithm reaches 39.2 frame/s, which is only 2.2 frame/s lower than that of YOLOv3, meeting the requirements of real-time detection.

Key words: YOLOv3 algorithm, mask wearing detection, small-scale targets, channel attention, multiscale fusion, loss function

CLC Number:

TP391.41

WEI Mingjun, ZHOU Taiyu, JI Zhanlin, ZHANG Xinnan. Detection Method of Mask Wearing in Public Places Based on YOLOv3[J].Journal of Guangxi Normal University(Natural Science Edition), 2023, 41(1): 76-86.

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Detection Method of Mask Wearing in Public Places Based on YOLOv3

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