LAUSR

Defective fastener images detection is an essential task in the vision-based railway track safety inspection. Although existing methods have achieved some level of success, the detection accuracy in this field suffers from the defective fasteners being far less common than normal fasteners. One way to tackle this problem is to expand the defect sample. However, current state-of-the-art defective fastener generation methods mainly rely on generative adversarial networks or simply augment the defect data through traditional image processing. These methods may not be ideal as it is difficult to produce images with high quality and rich diversity at the same time. This paper proposes a new method for fastener sample generation that actively divides the sample generation into two independent parts: defective foregrounds generation and complete backgrounds generation. The key to this method is to generate foregrounds and backgrounds based on geometric constraint and image inpainting, respectively. Specifically, we adopt a skeleton mapping algorithm to directionally control the generated types of defective foregrounds. Meanwhile, an image inpainting network is employed to expand the background. The experiments show that this enables us to generate better-quality and richer-diversity images by combining deep learning and image processing advantages. To the best of our knowledge, our method is the first to achieve state-of-the-art performance, i.e., the classification accuracy reaches 97.97%, without using real defective fastener images during the defect classification network training process.