中圖分類(lèi)號(hào)： TP391.4 文獻(xiàn)標(biāo)識(shí)碼： A DOI：10.16157/j.issn.0258-7998.211870 中文引用格式： 王林，南改改. 基于深度學(xué)習(xí)的口罩佩戴檢測(cè)與跟蹤[J].電子技術(shù)應(yīng)用，2022，48(5)：21-26. 英文引用格式： Wang Lin，Nan Gaigai. Detection and tracking of mask wearing based on deep learning[J]. Application of Electronic Technique，2022，48(5)：21-26.
Detection and tracking of mask wearing based on deep learning
Wang Lin，Nan Gaigai
School of Automation and Information Engineering，Xi′an University of Technology，Xi′an 710048，China
Abstract： 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 function is changed to CIoU loss to reduce the regression error improve detection accuracy, and provides good conditions for the subsequent 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.
Key words : object detection；object tracking；mask wearing detection；YOLOv3；Deep SORT
