Automatic Gauge Detection via Geometric Fitting for Safety Inspection

IEEE Access

Wei Xiang¹  Xinyang Zeng²  Beichen Li²  Huanjing Yue²  Jingyu Yang² 

[1] College of Science and Engineering, James Cook University, Townsville, QLD, Australia;School of Electrical and Information Engineering, Tianjin University, Tianjin, China;

关键词: Computer vision;    object detection;    pressure gauges;   

DOI  :  10.1109/ACCESS.2019.2925087

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown