Abstract
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.
Highlights
Due to the characteristics of SF6 pointer meter such as waterproof, anti-electromagnetic interference and oil resistance, the meter is widely used in substations
The widely used SF6 gas-filled equipment pressure gauges are short and the pointer is not connected to the center of the dial, and the pointer position of such dials cannot be identified by traditional computer vision techniques.[1,2]
To address the above problems, this paper proposes a digital image processing and Mask-RCNN neural network-based pointer reading recognition method for short pointer SF6 pressure gauges, which performs image pre-processing such as image filtering, image RGB binarization and Canny edge detection on the captured SF6 pointer gauge images, and uses Mask-RCNN network to segment the processed images in The pointer feature information and the scale dial feature information are used to obtain the meter pointer reading by angle calculation
Summary
Due to the characteristics of SF6 pointer meter such as waterproof, anti-electromagnetic interference and oil resistance, the meter is widely used in substations. Most of the reading recognition for the pointer meter is judged by human eyes. To address the above problems, this paper proposes a digital image processing and Mask-RCNN neural network-based pointer reading recognition method for short pointer SF6 pressure gauges, which performs image pre-processing such as image filtering, image RGB binarization and Canny edge detection on the captured SF6 pointer gauge images, and uses Mask-RCNN network to segment the processed images in The pointer feature information and the scale dial feature information are used to obtain the meter pointer reading by angle calculation. The method is tested in a 220kV substation for 350 SF6 pressure gauges (180 of them are non-linear pointer) of bus bar, and the recognition accuracy reaches 100%, which successfully solves the problem that the scale readings of such gauges cannot be recognized at present. The problem that the scale reading of this type of instrument cannot be identified has been successfully solved
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