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

Abstract

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 and then propose a novel global optimization framework to combine evidence from multiple cameras, Global Positioning System, and 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 the tie level and identifying anchor pattern exceptions at the compliance level. Quantitative analysis performed on a large video data set captured with different track and lighting conditions, as well as on a realtime field test, has 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 for detecting all three rail components, and a 100% detection rate is achieved for compliance-level anchor exception with three 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.

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“…Ying et al, proposed a rail detection component for automated monitoring and evaluation approach in rail systems [15]. The proposed method, was operated by a train watching with 16km/h.…”

Block Sparse Low-rank Matrix Decomposition based Visual Defect Inspection of Rail Track Surfaces

“…Therefore, automated and nondestructive inspection systems are strongly recommended for practical reasons [4]. For example, Fiber Bragg grating sensors [5,6], ultrasonic inspection [7], Eddy currents [8,9], MEMS gyroscope sensors [10], and visual inspection system (VIS) [11][12][13]. Ultrasonic inspection is very suited to internal crack detection, but the speed is very slow and it cannot detect surface defects in rail tracks.…”

A peak detection algorithm adopting magnetic sensor signal for rail spike location in tamping machine

“…When fasteners' positions and the switch are not at the same level line or fasteners themselves are damaged and missing, identification performance will also be affected. In addition, computer vision technology can also be applied to the identification of sleepers and fasteners, [4][5][6][7][8] but this technology is based on the images taken during tamping operation. If the shooting light is dim or sleeper itself is covered by ballast, the identification becomes also difficult.…”