An Outdoor Support Insulator Surface Defects Segmentation Approach via Image Adversarial Reconstruction in High-Speed Railway Traction Substation

As a result of high-voltage electricity and outdoor environment in high-speed railway traction substation, insulator surface defects are inevitable. However, most of the current unsupervised learning visual detection methods can only achieve patch-level defects detection. To achieve the goal of defects pixel-level segmentation, a universal three-step outdoor support insulators surface defects segmentation framework is proposed to segment insulators surface defects in this article. In this framework, outdoor support insulators are first located using the robust SCRDet network, which can locate the insulators accurately toward various angles. Then, a resolution-improved insulator reconstruction network (RIIR-Net) is proposed to reconstruct resolution-improved nondefect insulators. Finally, defective areas of insulators, such as missing, porcelain drop, and flashover, can be segmented via the proposed coarse-to-fine defects decision strategy, which can postprocess the residual map between the original defective insulator and the reconstructed nondefect insulator to effectively filter noise and segment defects based on decision rules of color feature, area, and HuMoment. Experiments on dataset collected from the Heishan traction substation demonstrate that the newly designed RIIR-Net can indeed improve the quality of reconstructed normal insulators, and the proposed framework can accurately achieve the task of outdoor support insulators defects pixel-level segmentation, including but not limited to missing, porcelain drop, and flashover.