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

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.