Rail Component Detection, Optimization and Assessment for Automatic Rail Track Inspection

In this paper, we present a real-time automatic vision-based rail inspection system, which performs inspections at 16 km/h with a frame rate of 20 fps. The system robustly detects important rail components such as ties, tie plates and anchors with high accuracy and efficiency. To achieve this goal, we first develop a set of image and video analytics, then propose a novel global optimization framework to combine evidence from multiple cameras, GPS (Global Positioning System) and DMI (Distance Measurement Instrument) to further improve the detection performance. Moreover, as the anchor is an important type of rail fastener, we have thus advanced the effort to detect anchor exceptions, which includes assessing the anchor conditions at tie level and identifying anchor pattern exceptions at compliance level.

Quantitative analysis performed on a large video data set captured with different track and lighting conditions as well as on a real-time field test, have demonstrated very encouraging performance on both rail component detection and anchor exception detection. Specifically, an average of 94.67% precision and 93% recall rate has been achieved on detecting all three rail components, and a 100% detection rate is achieved for compliance-level anchor exception with 3 false positives per hour. To our best knowledge, our system is the first to address and solve both component and exception detection problems in this rail inspection area.