Unmanned aerial vehicle transmission defect detection technology based on edge computing

Defect identification of transmission lines has become a crucial step in ensuring the proper functioning of the current transmission system due to the ongoing growth of the power grid size. The study primarily concerns itself with the current shortcomings of unmanned aerial vehicle transmission defect detection, particularly in terms of image quality and other related issues. In response, an unmanned aerial vehicle transmission defect detection system based on edge computing has been proposed. This system employs edge computing networks and lightweight improvements, and finally, through the analysis of experimental data, the performance and detection effectiveness of the system are validated. The outcomes revealed that the accuracy of the model used for the study in detecting insulators is 0.05 higher than other models. The system was more effective in detecting normal insulators and abnormal insulators. The error of the system in detecting transmission line images was 0.18 lower than other algorithms, and the average percentage error was 0.20 lower compared to other model error values. This reveals that the system used in the study was able to improve the detection of transmission lines, and also improved the quality of the detected images. This is an excellent manual for enhancing UAV transmission line defect detection precision in the future.