RAILCAR DETECTION, IDENTIFICATION AND TRACKING FOR RAIL YARD MANAGEMENT

We present a video analytics system combining railcar detection, classification, Federal Railroad Admin. (FRA) text identification, and logo detection into a system for locomotive transportation and yard management. Existing RFID-based systems are limited by sensor deployment and cannot visually identify railcars when they are away. As there are typically tens of tracks and hundreds of railcars in a yard, an automatic vision system is desirable. The proposed AI system is developed for autonomous yard inventory checking, such that the arrival, departure, and movement of individual railcars can be automatically monitored and managed in the facility. Our system consists of multiple cameras with edge computing devices installed at check points (track entrances and branches), such that visual detection and tracking of railcars can be performed and meta-data can be exchanged. After knowing the railcar locations and types, scene text detection is performed to search and recognize FRA ID markings and logos that can uniquely identify each railcar. Information fusion a database in the central hub can further improve railcar identification and reduce errors. Early results on real-world field collected data demonstrate the efficacy of the proposed approach.