Robust Fastener Detection for Autonomous Visual Railway Track Inspection

Abstract

Fasteners are critical railway components that maintain the rails in a fixed position. Their failure can lead to train derailments due to gage widening or wheel climb, so their condition needs to be periodically monitored. Several computer vision methods have been proposed in the literature for track inspection applications. However, these methods are not robust to clutter and background noise present in the railroad environment. This paper proposes a new method for fastener detection by 1) carefully aligning the training data, 2) reducing intra-class variation, and 3) bootstrapping difficult samples to improve the classification margin. Using the histogram of oriented gradients features and a combination of linear SVM classifiers, the system described in this paper can inspect ties for missing or defective rail fastener problems with a probability of detection of 98% and a false alarm rate of 1.23% on a new dataset of 85 miles of concrete tie images collected in the US Northeast Corridor (NEC) between 2012 and 2013. To the best of our knowledge, this dataset of 203,287 crossties is the largest ever reported in the literature.