Anomalous tie plate detection for railroad inspection

Abstract

This paper describes our latest work on identifying anomalous tie plates to automate railroad inspection using machine vision technology. Specifically, we have developed a completely automatic detection scheme to recognize tie plates with anomalous spiking patterns using various video analytics. In particular, each tie plate is first represented by four characteristic regions-of-interest (ROI), then each ROI is fed into a pre-trained SVM (Support Vector Machine) model, and classified to be either spike- or spike hole-related. Next, the dissimilarity between the current tie plate and a reference set of tie plates in a sliding window is measured and analyzed. Based on that, it is finally recognized as either an anomalous or a normal tie plate. Preliminary experiments conducted on a set of videos captured by our own designed imaging system, has achieved an average precision, recall and false alarm rates of 88%, 92.8% and 2.16%, respectively. This validates the promising direction of applying machine vision technology to assist in railroad inspection. © 2012 ICPR Org Committee.