Research on the Detection Algorithm of Pointer Instrument Based on YOLOv7

In view of the problems of difficulty in locating instruments and low detection accuracy in the detection and recognition of pointer instruments in complex environments, this paper proposes a complex environment instrument recognition method based on YOLOv7. In order to efficiently monitor the status of pointer instruments, a pointer instrument detection algorithm suitable for inspection robots is proposed. The collected instrument images are detected by the improved YOLOv7 model, and the detected and cropped images are used as the input images of the model. At the same time, the input images are rotated and corrected to make the model suitable for multi-angle instrument recognition. The test results show that the detection accuracy of pointer instruments in complex environments reaches 95.6%.