Abstract: Since power line insulator defects can easily lead to transmission system failures,it is critical to study defect detection algorithms.Traditional detection methods can only accurately locate insulators and detect faults with sufficient prerequisite knowledge,low interference,or under specific conditions.For automatically locating insulators and detecting insulator defects in UAV aerial images,we propose a novel deep Convolutional Neural Network(CNN) architecture that not only locates insulators but also detects insulator defects.The architecture is divided into two modules,the first module for insulator localization is responsible for detecting all insulators in the image,and the second module for insulator defect detection is responsible for detecting all insulator defects in the image,using a CNN with a Region Proposal Network(RPN) to convert insulator defect detection into a two-level object detection problem.Finally,we perform experiments using real datasets with defect detection accuracy and recall rates of 91.2% and 95.6%,respectively,satisfying the robustness and accuracy requirements.

Key words: Aerial image, Convolutional neural network(CNN), Defect detection, Insulator