PCSGAN: A Perceptual Constrained Generative Model for Railway Defect Sample Expansion From a Single Image

Many deep learning based railway defect detection methods have been proposed in recent years. They have greatly improved the efficiency and accuracy of defects detection. However, detection of railway defects remains challenging because of the limited number and types of defect samples, thus, general deep learning methods cannot be applied. In this paper, we designed a "Perceptually Constrained Single Image Generative Adversarial Network" (PCSGAN) to expand the number of railway defect image samples. PCSGAN uses a pyramidal structure to learn the internal features of a single image. In addition, we proposed a masking and a perceptual reconstruction loss mechanism to impose specific positional and structural constraints on the images. We tested the method using railway defects images and compared it to other single image generation models. The experiment results show that the images generated by PCSGAN take into account railway prior knowledge, generate railway structure which satisfied the constraints imposed by railway infrastructure designs, and also provide new information. High image realism and lowest Single Image FID were obtained, and the effectiveness of PCSGAN in the defect detection task were also validated.