基于深度学习的口罩佩戴检测与跟踪

Detection and tracking of mask wearing based on deep learning

佩戴口罩可以有效预防病毒的传播,为减少通过人工方式检查口罩佩戴情况所消耗的大量人力资源,提出一种基于深度学习的口罩佩戴检测与跟踪方法,该方法分为检测和跟踪两个模块。检测模块在YOLOv3网络的基础上引入空间金字塔池化结构,实现不同尺度的特征融合;然后将损失函数改为CIoU损失,减少回归误差,提升检测精度,为后续跟踪模块提供良好的条件。跟踪模块采用多目标跟踪算法Deep SORT,对检测到的目标进行实时跟踪,有效防止重复检测,改善被遮挡目标的跟踪效果。测试结果表明,该方法的检测速度为38 f/s,平均精度值达到为85.23%,相比原始YOLOv3算法提高了4%,能达到实时检测口罩佩戴情况的效果。

Wearing a mask can effectively prevent the spread of the virus. In order to reduce the consumption of a large number of human resources in manual inspection of mask wearing, this paper proposes a method of mask wearing detection and tracking based on deep learning, which is divided into two modules : detection and tracking. Based on the YOLOv3 network, the spatial pyramid pooling structure is introduced into the detection module to realize the feature fusion at different scales, then the loss func-tion is changed to CIoU loss to reduce the regression error improve detection accuracy, and provides good conditions for the subse-quent tracking module. The tracking module adopts the multiple object tracking algorithm Deep SORT to track the detected objects in actual time, which can effectively avoid repeated detection and better the tracking effect of the occluded targets. The test results indicate that the detection velocity of this way is 38 f/s, and the average accuracy value is 85. 23 %, which is 4 % higher than the original YOLOV3 algorithm, and can achieve the effect of real-time detection of mask wearing.