基于Mask-YOLO的复杂场景口罩佩戴检测

针对公共场所口罩佩戴检测存在遮挡、密集和小尺度的情况而导致检测精度不高的问题,以实时目标检测算法YOLOv3为基础提出一种Mask-YOLO算法.首先在特征融合过程中引入通道注意力机制以突出重要特征,减少了融合后冗余特征的影响,有效提高了特征利用率;然后以完全交并比(complete intersection over...

Mask Wearing Detection in Complex Scenes Based on Mask-YOLO

Aiming at the problem of low detection accuracy caused by occlusion, density and small scale in mask wearing detection in public places, a Mask-YOLO algorithm is proposed based on real-time target detection algorithm YOLOv3. First, the algorithm introduces channel attention mechanism in the process of feature fusion, effectively highlights the important features, reduces the influence of redundant features after fusion, and effectively improves the feature utilization. Then, complete intersection over union (CIoU) loss is used instead of mean square error (MSE) as the loss function of frame regression to improve the positioning accuracy. Finally, in addition to the cases of detecting wearing and not wearing masks, incorrect wearing of masks is also detected. Experimental results show that Mask-YOLO algorithm improves mean average precision (mAP) by 4.78% when frame per second (FPS) decreases by only 1% compared with YOLOv3 algorithm. As compared with other mainstream target detection algorithms, Mask-YOLO algorithm also has better detection effect and robustness for mask wearing detection in complex scenes.