A Deep Generative Approach for Rail Foreign Object Detections via Semi-supervised Learning

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

3 Scopus Citations
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Original languageEnglish
Pages (from-to)459-468
Journal / PublicationIEEE Transactions on Industrial Informatics
Issue number1
Online published9 Feb 2022
Publication statusPublished - Jan 2023


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. © 2022 IEEE.

Research Area(s)

  • Foreign object detection, image analytics, Image reconstruction, Inspection, Object detection, Rail transportation, Rails, railway infrastructure, semi-supervised learning, Semisupervised learning, Training, transportation safety