Structural Defect Detection Technology of Transmission Line Damper Based on UAV Image

Overhead transmission lines suffer from extended exposure to harsh weather conditions. Metal dampers, a crucial protective fitting in the line, can effectively suppress the conductor's vibration energy and prevent Aeolian vibration and ice shedding. To ensure the safety of operation of the damper, we are proposing a detection method for structural defect damper based on spatial relationship. First, the unmanned aerial vehicle (UAV) aerial damper images are processed with relative total variation (RTV) transform to obtain an enhanced image with a smooth texture and prominent foreground main structure. Second, the enhanced image is corrected by rotation so that the conductor remains horizontal. Next, based on the endpoint coordinates of the conductor, a foreground preselection box for improved GrabCut segmentation is automatically generated to extract the object dampers. Finally, the spatial relationship between the damper components in the segmentation results is regarded as the motive force of the damper structural defect diagnosis model to detect damage, inversion, slight, and serious deformation defects in sequence. We analyzed the performance of the proposed method through actual field tests, and the results demonstrated that the identification accuracy of the method is 95.76% when applied to a small sample set, which is higher than other existing methods based on traditional image techniques and deep learning defect detection, and can effectively identify different structural defects of dampers and provide reliable data for transmission line condition monitoring.

Automated summary

We propose a detection method for structural defect dampers based on spatial relationship. By processing UAV aerial damper images and applying a detection model, we can effectively identify different structural defects and provide reliable data for transmission line condition monitoring.