This paper mainly studies the target detection of face masks. Based on yOLOV4-TINY algorithm, multi-scale feature fusion of FPN structure and feature enhancement network are used to optimize the algorithm structure. First of all, this paper uses the xml.etree.ElementTree (ET) module in python to read the xml file to obtain the key information such as < object > face parameters, < name > face mask wearing status, < bndbox > face bounding box diagonal two vertex coordinates and so on. According to the vertex coordinates (bndbox) in the xml file, the target box is marked on the original image, and the color state of the target box is distinguished according to the wearing state of < name > face mask. Finally, two pictures of "250.png" and "477.png" are selected to be displayed in the body of this text. Then, a face mask detection algorithm based on YOLOv4-Tiny is established. when training the model, the multi-task joint loss of mask detection task is optimized by combining CIOU loss function and label smoothing strategy, and the Mosaic data enhancement method and learning rate cosine annealing attenuation strategy are used to improve the convergence speed and robustness of the model. After many times of detection and verification, the experimental results show that the detection accuracy of the proposed algorithm model is 94.84% (with_mask), 96.04% (without_mask) and 94.86% (mask_weared_incorrect) respectively in the three categories of human face mask detection: with_mask, without_mask and mask_weared_incorrect. Its quantitative indicator < number of faces correctly classified > / < number of all faces included in the tag file >, mAP is as high as 95.25%, and the accuracy is greatly improved.

Ruisha Zhu, Linjun Zhao. Research and Application of Face Mask Detection Algorithm Based on YOLOV4-Tiny. Academic Journal of Computing & Information Science (2022), Vol. 5, Issue 1: 64-69. https://doi.org/10.25236/AJCIS.2022.050113.

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