Mask Wearing Detection Algorithm Based on Improved YOLOv3 in Complex Scenes
摘要
The transmission of COVID-19 virus through respiratory droplets can be effectively prevented by correct mask wearing.However,complex factors in natural scenes including occlusion,crowds,and small-scale targets frequently affect the detection of mask wearing.To solve the problem,this paper proposes a YOLOv3-based mask wearing detection algorithm for complex scenes.The DarkNet53 backbone network is improved based on the cross-stage partial network,which reduces the calculation consumption and increases the training speed.Then an improved spatial pyramid pooling structure is introduced into YOLOv3,and the top-down and bottom-up feature fusion strategies are used to optimize the multi-scale prediction network,so as to realize feature enhancement.In addition,CIoU is selected as the loss function.The distance between the centers of the target and the detection frame,their overlap ratio,and aspect ratio are considered.The experimental results show that compared with the YOLOv3 algorithm,the proposed algorithm improves the detection accuracy of human faces by 7.3% and that of mask wearing by 14.9%,and the detection speed is improved by 6FPS on average.