Improved YOLOv4 Detection Algorithm for Face Mask Wearing Detection
Under the situation of normalized epidemic prevention and control,mask wearing detection is a necessary operation in every public place.It is of great practical significance to use existing deep learning knowledge to carry out mask wearing detec-tion,which can liberate a lot of manpower and material resources.This paper improves the YOLOv4 algorithm on the basis of the mask wearing detection due to the fact that there are too many people and it is easy to block each other,resulting in false detection and missed detection.Firstly,K-means++algorithm is used to perform size clustering for real boxes in the data set to improve the fit-ting ability of the network.Secondly,on the basis of CIOU loss function,alpha-IOU loss function with better performance is used to optimize the training process.Finally,Soft-NMS algorithm is used to replace the original NMS algorithm to improve the mutual sup-pression of the prediction boxes due to the close distance.Experimental results show that the algorithm has higher detection accuracy in the data set of this paper,and can effectively carry out mask wearing detection.
万方数据
维普
