An Anomaly-Free Representation Learning Approach for Efficient Railway Foreign Object Detection

Tiange Wang, Zijun Zhang*, Min Xie*

*Corresponding author for this work

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

2 Citations (Scopus)

Abstract

Applying machine vision to facilitate railway anomaly detections faces a grand challenge in that anomalous samples for model training are insufficient due to their infrequent occurrence and wide diversity. An anomaly-free representation learning approach (ARLA) is developed in this article to realize a machine vision-powered railway foreign object detection (RFOD) that does not rely on anomalous samples. The ARLA consists of two components, a memory-suppress diffusion network module and a contrastive dissimilarity network. The former network module well considers the diversity of normal patterns and reconstructs high-fidelity normal images. The latter network module enables image-level and pixelwise foreign object detections based on well-defined dissimilarity scores and distance maps. The ARLA realizes an efficient RFOD, which leverages only normal images in training and does not compromise the detection performance at the inference stage. Improvements offered by ARLA in terms of pixelwise detection performance and model complexity against two groups of benchmarks have been consistently observed based on computational studies using the railway dataset.
Original languageEnglish
Pages (from-to)12125-12135
Number of pages11
JournalIEEE Transactions on Industrial Informatics
Volume20
Issue number10
Online published4 Jul 2024
DOIs
Publication statusPublished - Oct 2024

Funding

This work was supported in part by Shenzhen-Hong Kong-Macau Science & Technology Category C Project (SGDX20220530111205037), in part by the National Natural Science Foundation of China (72371215 and 72032005), in part by the Research Grant Council of Hong Kong (11200621 and 11201023), and in part by the Hong Kong Innovation and Technology Commission (InnoHK Project CIMDA) and Hong Kong Institute of Data Science under Project 9360163

Research Keywords

  • Rail transportation
  • Anomaly detection
  • Representation learning
  • Image reconstruction
  • Training
  • Noise
  • Rails
  • Anomaly-free
  • contrastive learning
  • diffusion models
  • image analytics
  • railway scenes

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