Intelligent Identification Method of Hydrophobic Grade of Composite Insulator Based on Efficient GA‐YOLO Former Network

Abstract

AbstractThe hydrophobia of composite insulators is a parameter closely related to insulation performance. As an essential index to measure the pollution flashover resistance of composite insulators, the hydrophobic grade is of great significance to maintaining transmission line insulators. To judge the hydrophobic grade of composite insulators accurately and efficiently and to solve the problem of difficult extraction of fine‐grained feature information and high similarity between classes in the data sets of anti‐hydrophobic grade recognition, an end‐to‐end intelligent identification method of the hydrophobic grade of composite insulators based on Efficient GA‐YOLO Former network is proposed. This method introduces Swin Transformer—a multi‐head self‐attention mechanism with shifted windows, into CNN, which helps the model better understand the multi‐scale global semantic information of images through cross‐layer interaction. The efficient BiFPN structure is introduced in the neck part to realize the bidirectional cross‐scale connection and weighted feature fusion in the feature extraction process. By integrating the GAM attention mechanism to amplify the significant cross‐dimensional acceptance region, the model can learn more subtle details that are more difficult to distinguish, improving model efficiency and detection accuracy. Combined with the target detection algorithm, a system for evaluating the hydrophobic grade of composite insulators on transmission lines is designed, which can realize the real‐time measurement of the hydrophobic grade of composite insulators and has practical engineering application value. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.