首个铁路感知基准测试，涵盖5项核心任务，助力智能列车视觉系统评估。
Railway Artificial Intelligence Learning Benchmark (RAIL-BENCH): A Benchmark Suite for Perception in the Railway Domain
- 构建铁路场景专用的5项感知挑战，覆盖轨道、物体、植被等任务。
- 提供真实数据集与公开排行榜，支持方法可复现对比。
- 提出新指标LineAP，更精准评估轨迹线段几何精度。
现有铁路基础设施上的自动驾驶依赖可靠的基于摄像头的感知技术，但铁路领域缺乏具备标准化评估协议的公开基准套件，难以实现方法间的可复现比较。本文提出RAIL-BENCH，首个面向铁路领域的感知基准测试套件。其包含五项挑战：轨道检测、目标检测、植被分割、多目标跟踪和单目视觉里程计，均针对铁路环境特性设计。RAIL-BENCH提供经过筛选的训练与测试数据集，覆盖多样化真实场景，配套评估指标与公开分数榜（https://www.mrt.kit.edu/railbench）。在轨道检测任务中，提出一种新型基于线段的平均精度指标LineAP，独立于实例分组评估折线预测的几何准确性，解决了现有线检测指标的关键局限。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。