Edge algorithm optimization of Faster R-CNN algorithm for fault identification of transmission lines

To improve the effectiveness of transmission line defect recognition, this paper studied a transmission line defect recognition method that utilized edge algorithms to optimize the Faster R-CNN algorithm. Transmission line images through drones were collected. Extreme median filtering algorithm to reduce noise was used. Faster R-CNN model was inputted and defect features were extracted. RPN network to determine target candidate regions was used. Faster R-CNN algorithm using edge algorithm was optimized to determine pixel gradient amplitude and suppress non maximum values. The model was trained to complete the identification of transmission line defects. The test results showed that the algorithm studied can improve the recognition accuracy of various major defect categories, with an accuracy rate of over 85%.