The Tension Measurement Method for Transmission Line Suspension Components Based on Image Recognition and Deep Learning

As an important component of transmission lines, the dynamic tension parameters of cable-type structures directly influence the safety and operation of the lines. However, conventional tension detection methods commonly suffer from issues such as insufficient measurement accuracy, poor environmental adaptability, and the inability to operate on live lines, making them unsuitable for complex working conditions. To address these issues, this paper utilizes visual image technology and Broadband Phase Motion Magnification (BPMM) to amplify the micro-vibration amplitude and enhance the vibration images of transmission line cable-type components under environmental excitation.Furthermore, this study develops a combined segmentation algorithm using the U-Net network architecture and level set loss entropy to accurately capture the centroid motion trajectory of cables, thereby precisely extracting the vibration displacement time series. Finally, spectrum analysis is applied to invert the self-vibration characteristic parameters of the components and establish a tension calculation model.Experimental verification shows that the proposed method can precisely capture the micro-vibration signals induced by environmental excitation. The tension calculation results, when compared to standard sensor data, have a deviation of no more than 8%. This method successfully establishes a non-contact, high-precision measurement system for cable-type components, providing a new technical pathway for intelligent monitoring during the construction and maintenance of transmission lines.