Artificial intelligence-aided railroad trespassing detection and data analytics: Methodology and a case study.

The railroad industry plays a principal role in the transportation infrastructure and economic prosperity of the United States, and safety is of the utmost importance. Trespassing is the leading cause of rail-related fatalities and there has been little progress in reducing the trespassing frequency and deaths for the past ten years in the United States. Although the widespread deployment of surveillance cameras and vast amounts of video data in the railroad industry make witnessing these events achievable, it requires enormous labor-hours to monitor real-time videos or archival video data. To address this challenge and leverage this big data, this study develops a robust Artificial Intelligence (AI)-aided framework for the automatic detection of trespassing events. This deep learning-based tool automatically detects trespassing events, differentiates types of violators, generates video clips, and documents basic information of the trespassing events into one dataset. This study aims to provide the railroad industry with state-of-the-art AI tools to harness the untapped potential of video surveillance infrastructure through the risk analysis of their data feeds in specific locations. In the case study, the AI has analyzed over 1,600 h of archival video footage and detected around 3,000 trespassing events from one grade crossing in New Jersey. The data generated from these big video data will potentially help understand human factors in railroad safety research and contribute to specific trespassing proactive safety risk management initiatives and improve the safety of the train crew, rail passengers, and road users through engineering, education, and enforcement solutions to trespassing.

Real-Time Plane Detection with Consistency from Point Cloud Sequences.

Real-time consistent plane detection (RCPD) from structured point cloud sequences facilitates various high-level computer vision and robotic tasks. However, it remains a challenge. Existing techniques for plane detection suffer from a long running time or the problem that the plane detection result is not precise. Meanwhile, labels of planes are not consistent over the whole image sequence due to plane loss in the detection stage. In order to resolve these issues, we propose a novel superpixel-based real-time plane detection approach, while keeping their consistencies over frames simultaneously. In summary, our method has the following key contributions: (i) a real-time plane detection algorithm to extract planes from raw structured three-dimensional (3D) point clouds collected by depth sensors; (ii) a superpixel-based segmentation method to make the detected plane exactly match its actual boundary; and, (iii) a robust strategy to recover the missing planes by utilizing the contextual correspondences information in adjacent frames. Extensive visual and numerical experiments demonstrate that our method outperforms state-of-the-art methods in terms of efficiency and accuracy.

Tunnel Deformation Inspection via Global Spatial Axis Extraction from 3D Raw Point Cloud.

Global inspection of large-scale tunnels is a fundamental yet challenging task to ensure the structural stability of tunnels and driving safety. Advanced LiDAR scanners, which sample tunnels into 3D point clouds, are making their debut in the Tunnel Deformation Inspection (TDI). However, the acquired raw point clouds inevitably possess noticeable occlusions, missing areas, and noise/outliers. Considering the tunnel as a geometrical sweeping feature, we propose an effective tunnel deformation inspection algorithm by extracting the global spatial axis from the poor-quality raw point cloud. Essentially, we convert tunnel axis extraction into an iterative fitting optimization problem. Specifically, given the scanned raw point cloud of a tunnel, the initial design axis is sampled to generate a series of normal planes within the corresponding Frenet frame, followed by intersecting those planes with the tunnel point cloud to yield a sequence of cross sections. By fitting cross sections with circles, the fitted circle centers are approximated with a B-Spline curve, which is considered as an updated axis. The procedure of "circle fitting and B-SPline approximation" repeats iteratively until convergency, that is, the distance of each fitted circle center to the current axis is smaller than a given threshold. By this means, the spatial axis of the tunnel can be accurately obtained. Subsequently, according to the practical mechanism of tunnel deformation, we design a segmentation approach to partition cross sections into meaningful pieces, based on which various inspection parameters can be automatically computed regarding to tunnel deformation. A variety of practical experiments have demonstrated the feasibility and effectiveness of our inspection method.