用单目视频实时识别轨道入侵，提升有轨电车行人安全
RailSafeNet: Visual Scene Understanding for Tram Safety
- 融合语义分割与目标检测，通过投影距离判断风险
- 在RailSem19数据集上实现65%的分割IoU和75.6%的检测mAP
- 适合智能交通、自动驾驶系统开发人员参考
有轨电车常在人群密集区域运行，人车交互安全问题突出，事故后果可能从轻伤到致命。本文提出RailSafeNet，一个基于单目视频的实时视觉理解框架，融合语义分割、目标检测与规则距离评估，用于识别轨道侵入行为。系统通过对比物体投影距离与标准1435mm轨距，对行人、骑车人、宠物等进行风险分类。在多样化的RailSem19数据集上，经过滤的SegFormer B3模型达到65%的交并比（IoU），微调后的YOLOv8在IoU阈值0.50下实现75.6%的平均精度（mAP）。该方法实现高精度、少标注的场景理解，可在危险情况升级前向司机预警。代码已开源。
原文摘要 · Abstract (English)
Tram-human interaction safety is an important challenge, given that trams frequently operate in densely populated areas, where collisions can range from minor injuries to fatal outcomes. This paper addresses the issue from the perspective of designing a solution leveraging digital image processing, deep learning, and artificial intelligence to improve the safety of pedestrians, drivers, cyclists, pets, and tram passengers. We present RailSafeNet, a real-time framework that fuses semantic segmentation, object detection and a rule-based Distance Assessor to highlight track intrusions. Using only monocular video, the system identifies rails, localises nearby objects and classifies their risk by comparing projected distances with the standard 1435mm rail gauge. Experiments on the diverse RailSem19 dataset show that a class-filtered SegFormer B3 model achieves 65% intersection-over-union (IoU), while a fine-tuned YOLOv8 attains 75.6% mean average precision (mAP) calculated at an intersection over union (IoU) threshold of 0.50. RailSafeNet therefore delivers accurate, annotation-light scene understanding that can warn drivers before dangerous situations escalate. Code available at https://github.com/oValach/RailSafeNet.
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