Fast Monocular Measurement via Deep Learning-Based Object Detection for Real-Time Gas-Insulated Transmission Line Deformation Monitoring
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...
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| Format: | Article |
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MDPI AG
2025-04-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/18/8/1898 |
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| author | Guiyun Yang Wengang Yang Entuo Li Qinglong Wang Huilong Han Jie Sun Meng Wang |
| author_facet | Guiyun Yang Wengang Yang Entuo Li Qinglong Wang Huilong Han Jie Sun Meng Wang |
| author_sort | Guiyun Yang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-686a67ff49df4aa497449bb85eb4c092 |
| institution | OA Journals |
| issn | 1996-1073 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj-art-686a67ff49df4aa497449bb85eb4c0922025-08-20T02:28:14ZengMDPI AGEnergies1996-10732025-04-01188189810.3390/en18081898Fast Monocular Measurement via Deep Learning-Based Object Detection for Real-Time Gas-Insulated Transmission Line Deformation MonitoringGuiyun Yang0Wengang Yang1Entuo Li2Qinglong Wang3Huilong Han4Jie Sun5Meng Wang6Department of Mechanical Engineering, North China Electric Power University, Baoding 071003, ChinaDepartment of Mechanical Engineering, North China Electric Power University, Baoding 071003, ChinaDepartment of Mechanical Engineering, North China Electric Power University, Baoding 071003, ChinaDepartment of Mechanical Engineering, North China Electric Power University, Baoding 071003, ChinaDepartment of Mechanical Engineering, North China Electric Power University, Baoding 071003, ChinaThe State Key Laboratory of Rolling and Automation, Northeastern University, Shenyang 110819, ChinaDepartment of Mechanical Engineering, North China Electric Power University, Baoding 071003, ChinaDeformation 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.https://www.mdpi.com/1996-1073/18/8/1898gas-insulated transmission lines (GILs)deep learningYOLOmonocular measurement |
| spellingShingle | Guiyun Yang Wengang Yang Entuo Li Qinglong Wang Huilong Han Jie Sun Meng Wang Fast Monocular Measurement via Deep Learning-Based Object Detection for Real-Time Gas-Insulated Transmission Line Deformation Monitoring Energies gas-insulated transmission lines (GILs) deep learning YOLO monocular measurement |
| title | Fast Monocular Measurement via Deep Learning-Based Object Detection for Real-Time Gas-Insulated Transmission Line Deformation Monitoring |
| title_full | Fast Monocular Measurement via Deep Learning-Based Object Detection for Real-Time Gas-Insulated Transmission Line Deformation Monitoring |
| title_fullStr | Fast Monocular Measurement via Deep Learning-Based Object Detection for Real-Time Gas-Insulated Transmission Line Deformation Monitoring |
| title_full_unstemmed | Fast Monocular Measurement via Deep Learning-Based Object Detection for Real-Time Gas-Insulated Transmission Line Deformation Monitoring |
| title_short | Fast Monocular Measurement via Deep Learning-Based Object Detection for Real-Time Gas-Insulated Transmission Line Deformation Monitoring |
| title_sort | fast monocular measurement via deep learning based object detection for real time gas insulated transmission line deformation monitoring |
| topic | gas-insulated transmission lines (GILs) deep learning YOLO monocular measurement |
| url | https://www.mdpi.com/1996-1073/18/8/1898 |
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