The automated inspection and detection of foreign objects help prevent potential accidents and train derailments. Most existing approaches focus on the detection with prior labels, such as categories and locations of objects, and do not directly address detecting foreign objects of unknown categories, which can appear anytime on the rail track site. In this article, we develop a deep generative approach for detecting foreign objects without predefining the scope of objects. The detection procedure consists of the following three steps: first, the model composed of an autoencoder and a discriminator is developed via adversarial training based on normal rail images only; second, the detection of abnormal rail images is implemented based on the anomaly score obtained via the trained autoencoder; and finally, foreign objects are detected by filtering the subtle dissimilarity in normal areas and highlighting abnormal areas. The effectiveness of the proposed framework for the rail foreign object detection is validated with images collected by a train equipped with visual sensors. Computational results demonstrate that our proposal is capable to achieve an impressive performance on detecting numerous foreign objects. Moreover, two groups of benchmarking methods are employed to verify the superiority of the proposed framework.

中文翻译：

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基于半监督学习的铁路异物检测深度生成方法

异物的自动检查和检测有助于防止潜在的事故和火车脱轨。大多数现有方法都侧重于使用先验标签进行检测，例如对象的类别和位置，而不是直接解决检测可能随时出现在轨道站点上的未知类别的异物。在本文中，我们开发了一种深度生成方法来检测异物，而无需预先定义对象的范围。检测过程包括以下三个步骤：首先，由自动编码器和鉴别器组成的模型仅通过基于正常铁路图像的对抗训练来开发；其次，基于经过训练的自编码器获得的异常分数实现异常铁路图像的检测；最后，通过过滤正常区域的细微差异并突出异常区域来检测异物。所提出的铁路异物检测框架的有效性通过配备视觉传感器的列车收集的图像进行验证。计算结果表明，我们的提议能够在检测大量异物方面取得令人印象深刻的性能。此外，采用两组基准测试方法来验证所提出框架的优越性。计算结果表明，我们的提议能够在检测大量异物方面取得令人印象深刻的性能。此外，采用两组基准测试方法来验证所提出框架的优越性。计算结果表明，我们的提议能够在检测大量异物方面取得令人印象深刻的性能。此外，采用两组基准测试方法来验证所提出框架的优越性。