In order to reduce the false detection rate of the electric inspection robot in identifying the pointer meter in the transformer substation and improve the accuracy of meter reading identification, a pointer meter detection method based on deep learning is proposed. By adding a residual module to the YOLOv4-tiny network to improve the robustness of the model and improvements to the Hourglass network, precise identification of pointer meter readings is achieved. In order to verify the effectiveness of the proposed method, the method is tested with the image data of the transformer substation and the test results are compared with other methods. The experimental results show that the missing rate of the proposed approach is 1. 25%, the localization accuracy is less than 1. 125%, the overall detection time was less than 0. 5 s. Compare with Hough line detection with ORB or U-NET, the average error of reading recognition is reduced by 70. 8% and 58. 8%. The method provides new ideas for meter reading identification of transformer substations.