The reading recognition algorithm of pointer meter in unmanned pumping station based on YOLOv5

Pengzhou Chen; Jianwei Cui

doi:10.1117/12.3005880

10 October 2023 The reading recognition algorithm of pointer meter in unmanned pumping station based on YOLOv5

Pengzhou Chen, Jianwei Cui

Proceedings Volume 12799, Third International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023); 1279918 (2023) https://doi.org/10.1117/12.3005880
Event: 3rd International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023), 2023, Kuala Lumpur, Malaysia

Abstract

The purpose of this paper is to study the reading recognition algorithm of pointer meters in unmanned pump stations based on YOLOv5. The algorithm includes two key stages: dial area detection, pointer recognition and reading conversion. In the stage of dial area detection, YOLOv5 algorithm is used to accurately detect the dial area in the pump station instrument image. In the stage of pointer recognition and reading conversion, Canny edge detection, Hough line detection and angle method are combined to convert the detected pointer information into instrument reading. Through experimental verification, the algorithm presented in this paper shows excellent performance in the reading recognition task of pointer meters in unmanned pump stations. The experimental results show that the algorithm can detect the dial area of pump station instruments in real time and with high accuracy, and successfully recognize and convert pointer information into readable instrument readings. This provides a feasible solution for the automatic management of the operation of the unmanned pump station.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Pengzhou Chen and Jianwei Cui "The reading recognition algorithm of pointer meter in unmanned pumping station based on YOLOv5", Proc. SPIE 12799, Third International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023), 1279918 (10 October 2023); https://doi.org/10.1117/12.3005880

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