DefGAN: Defect Detection GANs With Latent Space Pitting for High-Speed Railway Insulator

As a key component of the high-speed railway catenary, the insulator plays an important role in supporting the catenary and maintaining the insulation between the catenary and earth. Regular inspection using computer vision technology is an effective way to detect insulator defects and improve the catenary operation safety. However, achieving full automation of insulator defects detection is still a difficult task due to the defective sample scarcity and subtle defect features. To overcome the problem, this article proposes defect detection GANs (DefGANs) that consist of a denoising autoencoder, a discriminator, and a classifier to detect defects, which can generate defective samples from pitted latent representations, thereby improving the reliability of defect detection classifier. The proposed DefGAN can pit the latent representations of normal samples to generate defective samples. Our proposal mainly consists of two stages. In the first stage, we use cascaded deep segmentation networks (CDSNets) to extract the insulator from the catenary images. Then, the insulator area is sampled into small patches. In the second stage, the patches are fed into the proposed DefGAN to detect defects. Moreover, the defect score is determined by the anomaly probability of the classifier and reconstruction error of the denoising autoencoder. The effectiveness of our work is measured across the catenary data sets.