Abstract
In railway surface defect detection applications, supervised deep learning methods suffer from the problems of insufficient defect samples and an imbalance between positive and negative samples. To overcome these problems, we propose a lightweight two-stage architecture including the railway cropping network (RC-Net) and defects removal variational autoencoder (DR-VAE), which requires only normal samples for training to achieve defect detection. First, we design a simple and effective RC-Net to extract railway surfaces accurately from railway inspection images. Second, the DR-VAE is proposed for background reconstruction of railway surface images to detect defects by self-supervised learning. Specifically, during the training process, DR-VAE contains a defect random mask module (D-RM) to generate self-supervised signals and uses a structural similarity index measure (SSIM) as pixel loss. In addition, the decoder of DR-VAE also acts as a discriminator to implement introspective adversarial training. In the inference stage, we reduce the random error of reconstruction by introducing a distribution capacity attenuation factor, and finally use the residuals of the original and reconstructed images to achieve segmentation of the defects. The experiments, including core parameter exploration and comparison with other models, indicate that the model can achieve a high detection accuracy.