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.

通过利用机器学习和计算机视觉技术，这项研究提出了一种智能系统，通过非极大值抑制（NMS）集成模型以及 UNet 架构的分割技术，对铁路公路环境进行目标检测和火车驶近监测，从而提高公路铁路交叉口的安全性。