Abstract: In order to improve the efficiency of UAV inspection of overhead lines and the intelligent detection efficiency of rust defects of overhead line metal fitting, a deep learning-based inspection method for rust defects of overhead lines metal fitting is proposed. Due to the characteristics of large environmental background, small target, and large difference in shooting angle and shooting light in the intelligent detection of metal fittings rust defects of overhead transmission lines, this paper uses image preprocessing algorithm to expand the data set. And YOLO is replaced by MobileNet′s backbone feature extraction network to improve the generalization ability and robustness of the algorithm, and the actual inspection images are used for experimental testing. In the test set validation, when the confidence threshold is 0.5, the P value is 0.92, the R value is 0.84, and the A_P value is 91.34%. The results show that this method has a good detection effect on the rust defects of overhead line metal fittings, and can provide reference for the assessment of equipment health status.

Key words: overhead transmission lines, deep learning, UAV patrol, metal fitting rust detection, image processing