The traditional digital instrument recognition method has a large amount of computational amount, not enough real-time, low accuracy. This paper studies a meter identification method is studied in combination with deep learning and image processing. In order to reduce the amount of computation, YOLOv4 target detection network is used and GhostNet is adopted as YOLOv4 basic network. At the same time, the Depthwise Separable Convolution and Ghost module can be introduced in YOLOv4 to reduce the amount of parameters, and the H-Swish activation function is applied to increase the accuracy. In order to highlight color information in the image binarization process, a digital binarized method is studied based on color model multi-threshold segmentation. The main color of RGB image is enhanced, and then converts to an HSI image, and then the pixel point satisfying the condition will be reserved by multi-threshold processing, thereby obtaining a binarized image. Digital information can be better extracted in comparison with traditional image pretreatment algorithms. Experimental results show that the proposed method of reaches 87.98 mAP on the test data set, and detection speed is increased to 37.2 FPS, and then the effect is significant in digital instrument positioning and digital inspection.