Deployment of neural network algorithms via lightweight neural network algorithms to edge computing devices is the mainstream direction to solve the old meter reading problem.In this pa-per,we propose the YOLOv5s-Pointer lightweight network model for pointer meter detection,using the YOLOv5s network model as the basis,replacing the CSPDarkNet53 feature extraction network with the MobileNetV3 network by introducing the Mixup data enhancement technique,using the SLoU Loss to localize the loss function,and introducing the dynamic sample weighting idea to improve the net-work.The experimental results show that compared to YOLOv5s network the number of parameters and computation on the validation set are reduced by 78％and 57％,respectively,and the accuracy rate is improved by 1.3％.
pointer meter inspectionYOLOv5sdata enhancementMobileNetV3lightweighting
维普
万方数据
