In high-speed train safety inspection, two changed images which are derived from corresponding parts of the same train and photographed at different times are needed to identify whether they are defects. The critical challenge of this change classification task is how to make a correct decision by using bitemporal images. In this paper, two convolutional neural networks are presented to perform this task. Distinct from traditional classification tasks which simply group each image into different categories, the two presented networks are capable of inherently detecting differences between two images and further identifying changes by using a pair of images. In doing so, even in the case that abnormal samples of specific components are unavailable in training, our networks remain capable to make inference as to whether they become abnormal using change information. This proposed method can be used for recognition or verification applications where decisions cannot be made with only one image (state). Equipped with deep learning, this method can address many challenging tasks of high-speed train safety inspection, in which conventional methods cannot work well. To further improve performance, a novel multishape training method is introduced. Extensive experiments demonstrate that the proposed methods perform well.
A Convolutional Neural Network-Based Classification and Decision-Making Model for Visible Defect Identification of High-Speed Train Images
