Use of deep convolutional neural networks and change detection technology for railway track inspections

• Published in: Proceedings of the Institution of Mechanical Engineers. Part F, Journal of rail and rapid transit Journal of rail and rapid transit, 2023-02, Vol.237 (2), p.137-145
• Main Authors: Harrington, Ryan M, Lima, Arthur de O, Fox-Ivey, Richard, Nguyen, Thanh, Laurent, John, Dersch, Marcus S, Edwards, J Riley
• Format: Article
• Language: English
• Subjects: Artificial neural networks; Automation; Change detection; Fasteners; Federal regulation; Inspection; Inspections; Machine vision; Neural networks; Railway engineering; Railway tracks; Spikes; Vision systems
• ISSN: 0954-4097; 2041-3017
• DOI: 10.1177/09544097221093486

Railroad track inspections conducted in accordance with federal regulations and internal railway operating practices result in significant labor costs and occupy valuable network capacity. These factors, combined with advancements in the field of machine vision, have encouraged a transition from human visual inspections to machine-based alternatives. Commercial machine vision technologies for railway inspection currently exist, and automated analysis approaches—which deliver objective results—are available in some systems. However, they are limited to a “pass/fail” approach through the detection of components which fail to meet maintenance or geometry thresholds, as opposed to being able to detect subtle changes in track conditions to identify evolving problems. To overcome these limitations, this paper presents results from the field deployment and validation of a system that pairs three-dimensional (3D) machine vision with automated change detection technology. The change detection approach uses a deep convolution neural network (DCNN) to accurately characterize track conditions between repeat runs. Current automated track inspection technologies were studied, and the applicability of change detection is discussed. The paper presents the process for 3D image capture, DCNN training, and evaluation by comparing DCNN results to an expert human evaluator. Finally, it presents change detection results for fastener presence and spike height. Results indicate that this technology can successfully identify fasteners and spikes with percent accuracies greater than 98% and that it can successfully generate change detection results for comparison of track condition among runs.