摘要：Currently widely used SF6 inflatable equipment pressure gauge are short pointer and the pointer is not connected to the center of the dial (non-linear pointer), The position of the hands on this dial cannot be identified using traditional computer vision techniques. In order to solve the problem, This paper proposes a method for SF6 pressure gauge pointer and reading recognition based on digital image processing and Mask-RCNN neural network image segmentation technology. The method first pre-processes the SF6 pressure gauge image and Canny edge detection, while using Mask-RCNN network to extract the pointer feature information and scale feature information, and uses the SF6 pressure gauge pointer feature and scale feature to calculate the pressure gauge reading. The effectiveness of the algorithm is verified through the scale identification of 180 short pointer SF6 pressure meters actually operated in a 220kV substation with 100% accuracy.
