Railway Artificial Intelligence Learning Benchmark (RAIL-BENCH): A Benchmark Suite for Perception in the Railway Domain

AIPR assessment

This is a moderately hard and competitive problem in a niche but safety-critical domain, made harder by the lack of public railway data and the diversity of operational scenes. The paper's strengths reinforce each other: diverse real-world data, standardized evaluation, and a public benchmark site make the resource immediately useful to the community. The main weaknesses also compound somewhat, since the novelty is concentrated in benchmark construction and metric design rather than a deeper alg

Abstract

Automated train operation on existing railway infrastructure requires robust camera-based perception, yet the railway domain lacks public benchmark suites with standardized evaluation protocols that would enable reproducible comparison of approaches. We present RAIL-BENCH, the first perception benchmark suite for the railway domain. It comprises five challenges - rail track detection, object detection, vegetation segmentation, multi-object tracking, and monocular visual odometry - each tailored to the specific characteristics of railway environments. RAIL-BENCH provides curated training and test datasets drawn from diverse real-world scenarios, evaluation metrics, and public scoreboards (https://www.mrt.kit.edu/railbench). For the rail track detection challenge we introduce LineAP, a novel segment-based average precision metric that evaluates the geometric accuracy of polyline predictions independently of instance-level grouping, addressing key limitations of existing line detection metrics.

Score Breakdown

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