Automatic gauge detection via geometric fitting for safety inspection

Li, Beichen, Yang, Jingyu, Zeng, Xinyang, Yue, Huanjing, and Xiang, Wei (2019) Automatic gauge detection via geometric fitting for safety inspection. IEEE Access, 7. 8746263. pp. 87042-87048.

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Abstract

For safety considerations in electrical substations, the inspection robots are recently deployed to monitor important devices and instruments with the presence of skilled technicians in the high-voltage environments. The captured images are transmitted to a data station and are usually analyzed manually. Toward automatic analysis, a common task is to detect gauges from captured images. This paper proposes a gauge detection algorithm based on the methodology of geometric fitting. We first use the Sobel filters to extract edges which usually contain the shapes of gauges. Then, we propose to use line fitting under the framework of random sample consensus (RANSAC) to remove straight lines that do not belong to gauges. Finally, the RANSAC ellipse fitting is proposed to find most fitted ellipse from the remaining edge points. The experimental results on a real-world dataset captured by the GuoZi Robotics demonstrate that our algorithm provides more accurate gauge detection results than several existing methods.

Item ID: 61944
Item Type: Article (Research - C1)
ISSN: 2169-3536
Keywords: Computer vision, object detection, pressure gauges
Copyright Information: This work is licensed under a Creative Commons Attribution 3.0 License. For more information, see http://creativecommons.org/licenses/by/3.0
Date Deposited: 20 May 2020 02:25
FoR Codes: 46 INFORMATION AND COMPUTING SCIENCES > 4603 Computer vision and multimedia computation > 460304 Computer vision @ 100%
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