Intelligent Railroad Grade Crossing: Leveraging Semantic Segmentation
  and Object Detection for Enhanced Safety
Al Amin Deo Chimba Kamrul Hasan Emmanuel Samson
Crashes and delays at Railroad Highway Grade Crossings (RHGC), where highways
and railroads intersect, pose significant safety concerns for the U.S. Federal
Railroad Administration (FRA). Despite the critical importance of addressing
accidents and traffic delays at highway-railroad intersections, there is a
notable dearth of research on practical solutions for managing these issues. In
response to this gap in the literature, our study introduces an intelligent
system that leverages machine learning and computer vision techniques to
enhance safety at Railroad Highway Grade crossings (RHGC). This research
proposed a Non-Maximum Suppression (NMS)- based ensemble model that integrates
a variety of YOLO variants, specifically YOLOv5S, YOLOv5M, and YOLOv5L, for
grade-crossing object detection, utilizes segmentation techniques from the UNet
architecture for detecting approaching rail at a grade crossing. Both methods
are implemented on a Raspberry Pi. Moreover, the strategy employs
high-definition cameras installed at the RHGC. This framework enables the
system to monitor objects within the Region of Interest (ROI) at crossings,
detect the approach of trains, and clear the crossing area before a train
arrives. Regarding accuracy, precision, recall, and Intersection over Union
(IoU), the proposed state-of-the-art NMS-based object detection ensemble model
achieved 96% precision. In addition, the UNet segmentation model obtained a 98%
IoU value. This automated railroad grade crossing system powered by artificial
intelligence represents a promising solution for enhancing safety at
highway-railroad intersections.