Fast Monocular Measurement via Deep Learning-Based Object Detection for Real-Time Gas-Insulated Transmission Line Deformation Monitoring

Guiyun Yang, Wengang Yang (), Entuo Li, Qinglong Wang, Huilong Han, Jie Sun and Meng Wang
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Guiyun Yang: Department of Mechanical Engineering, North China Electric Power University, Baoding 071003, China
Wengang Yang: Department of Mechanical Engineering, North China Electric Power University, Baoding 071003, China
Entuo Li: Department of Mechanical Engineering, North China Electric Power University, Baoding 071003, China
Qinglong Wang: Department of Mechanical Engineering, North China Electric Power University, Baoding 071003, China
Huilong Han: Department of Mechanical Engineering, North China Electric Power University, Baoding 071003, China
Jie Sun: The State Key Laboratory of Rolling and Automation, Northeastern University, Shenyang 110819, China
Meng Wang: Department of Mechanical Engineering, North China Electric Power University, Baoding 071003, China

Energies, 2025, vol. 18, issue 8, 1-22

Abstract: Deformation monitoring of Gas-Insulated Transmission Lines (GILs) is critical for the early detection of structural issues and for ensuring safe power transmission. In this study, we introduce a rapid monocular measurement method that leverages deep learning for real-time monitoring. A YOLOv10 model is developed for automatically identifying regions of interest (ROIs) that may exhibit deformations. Within these ROIs, grayscale data is used to dynamically set thresholds for FAST corner detection, while the Shi–Tomasi algorithm filters redundant corners to extract unique feature points for precise tracking. Subsequent subpixel refinement further enhances measurement accuracy. To correct image tilt, ArUco markers are employed for geometric correction and to compute a scaling factor based on their known edge lengths, thereby reducing errors caused by non-perpendicular camera angles. Simulated experiments validate our approach, demonstrating that combining refined ArUco marker coordinates with manually annotated features significantly improves detection accuracy. Our method achieves a mean absolute error of no more than 1.337 mm and a processing speed of approximately 0.024 s per frame, meeting the precision and efficiency requirements for GIL deformation monitoring. This integrated approach offers a robust solution for long-term, real-time monitoring of GIL deformations, with promising potential for practical applications in power transmission systems.

Keywords: gas-insulated transmission lines (GILs); deep learning; YOLO; monocular measurement (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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