Defect Detection Technology of Tension Clamps Based on UAV

Jiafeng Chen, Hui Xu, Libo Liu, Guangze Zhu, Yanchen Cui, Zhicheng Wang, Lei Yu, Jiaqiang Sun, Kangxian Tang and Jilei Cai

GBP Proceedings Series, 2025, vol. 7, 46-50

Abstract: The tension clamp, a critical component of transmission lines, can lead to major safety incidents if its defects are not addressed. Traditional manual inspections are inefficient and risky. This paper introduces an intelligent inspection technology for tension clamps using drones, which employs a quad-copter equipped with high-definition visible light and infrared dual-sensor systems. By integrating autonomous flight path planning, this system can collect data from multiple angles at close range. For typical defects such as cracks, rust, and overheating, a two-stage recognition model has been developed, combining YOLOv5 object detection with an improved ResNet34 classification algorithm, and incorporating attention mechanisms to enhance the extraction of features from small targets. Experiments show that on a test set of 2,368 annotated images, the system achieves a positioning accuracy of 96.2%, an average defect recognition accuracy of 92.7%, and reduces the detection time for a single base tower to 8 minutes. This technology significantly enhances inspection efficiency and safety, offering a new solution for the intelligent operation and maintenance of transmission lines.

Keywords: UAV inspection; tension clamp; defect detection; deep learning; infrared thermal imaging; transmission line maintenance (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:axf:gbppsa:v:7:y:2025:i::p:46-50

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