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.

中文翻译：

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基于卷积神经网络的分类和决策模型用于高速列车图像的可见缺陷识别

在高速列车安全检查中，需要从同一列车的相应部分派生并在不同时间拍摄的两个变化图像，以识别它们是否为缺陷。这项变更分类任务的关键挑战是如何通过使用时空图像做出正确的决定。在本文中，提出了两个卷积神经网络来执行此任务。与传统的分类任务不同，传统的分类任务只是将每个图像分为不同的类别，这两个呈现的网络能够固有地检测两个图像之间的差异，并通过使用一对图像进一步识别变化。这样一来，即使在训练中无法获得特定成分的异常样本的情况下，我们的网络仍然能够使用更改信息推断它们是否变得异常。此提议的方法可用于无法仅凭一个图像（状态）做出决定的识别或验证应用。配备深度学习的方法可以解决高速火车安全检查的许多艰巨任务，而常规方法无法很好地解决这些问题。为了进一步提高性能，引入了一种新颖的多形训练方法。大量实验表明，所提出的方法表现良好。传统方法无法很好地解决这些问题。为了进一步提高性能，引入了一种新颖的多形训练方法。大量实验表明，所提出的方法表现良好。传统方法无法很好地解决这些问题。为了进一步提高性能，引入了一种新颖的多形训练方法。大量实验表明，所提出的方法表现良好。