针对新冠肺炎防控期间肉眼识别判断行人是否佩戴口罩效率低且存在较大风险的问题, 提出一种改进检测目标边框损失的自然场景下行人是否佩戴口罩的检测算法. 该算法对YOLOv3损失函数进行改进, 应用GIoU计算目标边界框损失, 完成自然场景下行人是否佩戴口罩的检测. 算法在开源的WIDER FACE数据集和MAFA数据集上训练, 采集自然场景图片进行测试, 行人是否佩戴口罩的mAP (mean Average Precision)达到了88.4%, 取得了较高的检测准确率, 在自然场景视频检测中平均每秒传输帧数达到38.69, 满足实时检测的要求.
It is inefficient and highly risky to identify whether pedestrians are wearing a mask or not through naked eyes during the prevention and control of the COrona VIrus Disease 2019 (COVID-19). To solve this, we devise an algorithm to detect whether the pedestrians are wearing masks in the natural scenes with the improvement in the loss function of bounding box regression. The algorithm improves the YOLOv3 loss function and uses GIoU to calculate the bounding box loss to detect whether pedestrians wear masks in natural scenes. The algorithm is trained on the open-source WIDER FACE dataset and MAFA dataset. When the natural scene pictures are collected for testing, the mAP (mean Average Precision) of whether pedestrians wear masks is as high as 88.4%. In the detection of natural scene videos, the average number of frames per second is 38.69, which meets the requirements of real-time detection.