Automatic Detection of Ballast Unevenness Using Deep Neural Network

Abstract

The amount of freight transported by rail and the number of passengers are increasing year by year. Any disruption to the passenger or freight transport stream can generate both financial and human losses. Such a disruption can be caused by the rail infrastructure being in poor condition. For this reason, the state of the infrastructure should be monitored periodically. One of the important elements of railroad infrastructure is the ballast. Its condition has a significant impact on the safety of rail traffic. The unevenness of the ballast surface is one of the indicators of its condition. For this reason, a regulation was introduced by Polish railway lines specifying the maximum threshold of ballast unevenness. This article presents an algorithm that allows for the detection of irregularities in the ballast. These irregularities are determined relative to the surface of the sleepers. The images used by the algorithm were captured by a laser triangulation system placed on a rail inspection vehicle managed by the Polish railway lines. The proposed solution has the following elements of novelty: (a) it presents a simple criterion for evaluating the condition of the ballast based on the measurement of its unevenness in relation to the level of the sleeper; (b) it treats ballast irregularity detection as an instance segmentation process and it compares two segmentation algorithms, Mask R-CNN and YOLACT, in terms of their application to ballast irregularity detection; and (c) it uses segmentation-related metrics—mAP (Mean Average Precision), IoU (Intersection over Union) and Pixel Accuracy—to evaluate the quality of the detection of ballast irregularity.

Keywords

• deep learning
• object detection
• image segmentation
• railway track diagnostics