Automatic Reading Method for Analog Dial Gauges with Different Measurement Ranges in Outdoor Substation Scenarios

In substation working environments, analog dial gauges are widely used for equipment monitoring. Accurate reading of dial values is crucial for real-time understanding of equipment operational status and enhancing the intelligence of substation equipment operation and maintenance. However, existing dial reading recognition algorithms face significant errors in complex scenarios and struggle to adapt to dials with different measurement ranges. To address these issues, this paper proposes an automatic reading method for analog dial gauges consisting of two stages: dial segmentation and reading recognition. In the dial segmentation stage, an improved DeepLabv3+ network is used to achieve precise segmentation of the dial scale and pointer, and the network is made lightweight to meet real-time requirements. In the reading recognition stage, the distorted image is first corrected, and PGNet is used to obtain scale information for scale matching. Finally, an angle-based method is employed to achieve automatic reading recognition of the analog dial gauge. The experimental results show that the improved Deeplabv3+ network has 4.25 M parameters, with an average detection time of 19 ms per image, an average Pixel Accuracy of 92.7%, and an average Intersection over Union (IoU) of 79.7%. The reading recognition algorithm achieves a reading accuracy of 92.3% across dial images in various scenarios, effectively improving reading recognition accuracy and providing strong support for the development of intelligent operation and maintenance in substations.