中圖分類號(hào)：TP391 文獻(xiàn)標(biāo)志碼：A DOI: 10.16157/j.issn.0258-7998.223582 中文引用格式： 段高峰，單劍鋒，劉哲. 復(fù)雜環(huán)境下輕量化口罩佩戴檢測(cè)算法研究[J]. 電子技術(shù)應(yīng)用，2023，49(8)：108-113. 英文引用格式： Duan Gaofeng，Shan Jianfeng，Liu Zhe. Research on lightweight detection algorithm of wearing mask in complex environment[J]. Application of Electronic Technique，2023，49(8)：108-113.
Research on lightweight detection algorithm of wearing mask in complex environment
Duan Gaofeng，Shan Jianfeng，Liu Zhe
(College of Electronic and Optical Engineering & College of Flexible Electronics （Future Technology）， Nanjing University of Posts and Telecommunications， Nanjing 210023，China)
Abstract： In view of the huge computational load of the current YOLOv4 algorithm, it is difficult to meet the real-time requirements of the mask wearing detection system, a lightweight detection algorithm (Light-YOLOv4) was proposed. The GhostNet network structure integrating ECA attention mechanism was replaced by the backbone network of YOLOv4 to reduce the number of parameters. Using dilated convolution and SPPF for reference, ASPPFCSPC structure is proposed to replace SPP effectively to increase the receptive field. In order to solve the overlapping problem caused by too dense targets, the RepBox loss function is added on the basis of the original, so that the prediction boxes of different targets are far away from each other to reduce the missed detection. The experiment shows that the mAP value of the Light-YOLOv4 algorithm is 94.2%, FPS is 46.3 frames, and the model size is 95 MB. Compared with the mAP value of YOLOv4 algorithm, the detection rate is only reduced by 1.1%, the detection rate is increased by 51.8%, the number of parameters is reduced by 70.0%, and the model size is reduced by 61.1%, friendly to low performance detection equipment.
Key words : YOLOv4；GhostNet；efficient channel attention；RepBox loss
