Component-based track inspection using machine-vision technology

In this paper, we present our latest research engagement with a railroad company to apply machine vision technologies to automate the inspection and condition monitoring of railroad tracks. Specifically, we have proposed a complete architecture including imaging setup for capturing multiple video streams, important rail component detection such as tie plate, spike, anchor and joint bar bolt, defect identification such as raised spikes, defect severity analysis and temporal condition analysis, and long-term predictive assessment. This paper will particularly present various video analytics that we have developed to detect rail components, which form the building block of the entire framework. Our preliminary performance study has achieved an average of 98.2% detection rate, 1.57% false positive rate and 1.78% false negative rate on the component detection. Finally, with the lack of sufficient representative data and annotations to evaluate system performance on exception detection at both sequence and compliance levels, we proposed a mathematical modeling approach to calculate the probabilities of detecting such exceptions. Such analysis shows that there is still big room for us to improve our approaches in order to achieve desired false positive rate and miss detection rate at the sequence level.