Abstract: In order to improve the recognition accuracy and generalization ability of pointer meters, an automatic reading method of pointer meters based on improved YOLOv4-Tiny is proposed. The first level model of this method is based on the YOLOv4-Tiny algorithm to extract the dial area of pointer instrument and instrument classification. The detection accuracy of the original YOLOv4-Tiny model is improved by deepening the main feature extraction network, adding the FPN structure and detection head, and using the PReLU activation function. The second reading algorithm uses PPHT and least square method to fit the pointer, and deduces the meter reading through the slope. The experimental results show that the improved method can read the pointer meter effectively and accurately. Compared with the original network, the improved YOLOv4-Tiny model improves the F1 score by 10.65 percentage points, reaches 93.31%, FPS reaches 192, and the overall recognition accuracy reaches 99.36%.