A Semantic Communication-Enabled Edge-Cloud Collaborative Visual Perception System for Smart Railways

Next-generation smart railways require reliable real-time visual perception to ensure operational safety. However, continuously transmitting high-resolution raw video from onboard cameras to the cloud is often infeasible due to limited wireless bandwidth and fluctuating connectivity along rail lines. To address this challenge, this article presents a semantic-driven edge-cloud collaborative perception system that reduces communication overhead by transmitting task-relevant semantic masks instead of raw image frames. A lightweight semantic segmentation model deployed at the train edge extracts compact mask representations, which are further compressed and transmitted to the cloud for global risk analysis and decision support. A RailSem19-based case study demonstrates that the proposed semantic transmission mechanism significantly reduces uplink data volume and end-to-end latency under bandwidth-constrained conditions, while preserving high semantic fidelity for safety-critical railway objects. The results validate the effectiveness of communication-aware perception design for railway environments and highlight its potential for scalable deployment in next-generation intelligent transportation systems.