关键词: 口罩佩戴检测, YOLOv4-tiny, Max Module结构, 多尺度融合, CIoU

Abstract:

Aiming at the problem of insufficient feature extraction and low feature utilization in mask wearing detection tasks in the current YOLO lightweight network, a lightweight network algorithm based on improved YOLOv4-tiny is proposed. It increases the Max Module structure to obtain more main features of the target and improves the detection accuracy. A bottom-up multi-scale fusion is proposed, which combines low-level information to enrich the feature level of the network to improve feature utilization. It uses CIoU as the bounding box regression loss function to speed up model convergence. Compared with the original algorithm, in the public data set PASCAL VOC and mask wearing detection tasks, mAP is increased by 4.9 percentage points and 3.3 percentage points, respectively, and the detection rate reaches 74 frame/s and 64 frame/s, respectively, which meets the accuracy and real-time performance of mask wearing detection tasks.

Key words: mask wearing detection, YOLOv4-tiny, Max Module structure, multi-scale fusion, CIoU