基于改进的YOLOv3口罩佩戴检测和识别

新冠疫情仍在全球肆虐，佩戴口罩可以有效阻断新冠病毒传播，口罩佩戴检测系统能及时提醒公共场所活动的人佩戴口罩。针对该问题及小尺度目标检测困难的问题，提出了一种基于YOLOv3改进的网络模型Face_mask Net用于口罩佩戴检测。由于YOLOv3算法训练的网络模型对小目标检测率低,IoU值相同时不能反映预测框和目标框是否相交，以及传统NMS对于遮挡经常产生错误抑制情况，Face_mask Net改进了残差块和神经网络结构，引入SPP模块和CSPNet网络模块，并采用DIoU作为损失函数,DIoU-NMS算法作为分类器。实验结果表明，Face_mask Net可以有效提高目标检测准确率，AP75下的平均准确率为58.05%，相比由YOLOv3算法训练的网络模型提高了4.11%。

Mask wearing detection and recognitionbased on the improved YOLOv3

The COVID-19 epidemic is still rampant around the world. Wearing masks can effectively block the spread of novel coronavirus, while mask wearing detection can timely remind people in public places to wear masks. To solve the problem and the difficulty of small scale target detection, an improved network model Face_mask Net based on the YOLOv3 algorithm is proposed for mask wearing detection. Because the network model trained by the YOLOv3 algorithm has a low detection rate of small targets,the same IoU value cannot reflect whether the prediction frame and the target frame intersect, and the traditional NMS often produces false suppression for occlusion, the algorithm in this paper improves the residual block and neural network structure, introduces SPP module and CSPNet network module, and adopt DIoU as the loss function and DIoU-NMS as the classifier. The experimental results show that Face_mask Net can effectively improve the target detection accuracy, and the average accuracy of AP75 is 58.05%, which is 4.11 percentage points higher than that of the network model trained by the Yolov3 algorithm.