Developing a Portable Railroad Crossing Monitoring System Based on Artificial Intelligence and Image Processing Technology

The objective of this proposal is to create a cost-effective, field-deployable system capable of identifying, counting, and categorizing a diverse range of objects, including vehicles, pedestrians, and other foreign obstructions, at railroad grade crossings. This system also aims to supply crucial data for collision warnings, as well as inform future traffic management and urban planning initiatives. The cornerstone of a successful intelligent railroad grade crossing monitoring system lies in precise object detection, counting, and classification capabilities. To achieve this, the research team proposes the development of a specialized deep neural network (DNN) augmented with a custom detection algorithm. This network will operate in conjunction with an edge computing platform and commercially available cameras to identify potential hazards at grade crossings in real-time. Powered by batteries for enhanced portability, the system can be strategically deployed at specific crossings based on situational needs. Beyond basic detection, the proposed system will also excel in object classification, segregating detected objects into distinct categories such as pedestrian, vehicle, tree, or package. This nuanced classification will enable a shift from current “passive” warning mechanisms to a more “proactive” traffic management strategy. By recognizing and categorizing potential hazards, local agencies will be better equipped to make informed decisions for urban development, thereby mitigating trespassing risks by targeting their sources directly.