Detecting obstacles in railway scenarios is both crucial and challenging due to the wide range of obstacle categories and varying ambient conditions such as weather and light. Given the impossibility of encompassing all obstacle categories during the training stage, we address this out-of-distribution (OOD) issue with a semi-supervised segmentation approach guided by optical flow clues. We reformulate the task as a binary segmentation problem instead of the traditional object detection approach. To mitigate data shortages, we generate highly realistic synthetic images using Segment Anything (SAM) and YOLO, eliminating the need for manual annotation to produce abundant pixel-level annotations. Additionally, we leverage optical flow as prior knowledge to train the model effectively. Several experiments are conducted, demonstrating the feasibility and effectiveness of our approach.

翻译：铁路场景中的障碍物检测至关重要且极具挑战性，这源于障碍物类别繁多以及天气、光照等环境条件多变。鉴于在训练阶段不可能涵盖所有障碍物类别，我们提出一种由光流线索引导的半监督分割方法来解决这一分布外（OOD）问题。我们将该任务重新定义为二值分割问题，而非传统的目标检测方法。为缓解数据短缺，我们利用Segment Anything（SAM）和YOLO生成高度逼真的合成图像，无需人工标注即可产生丰富的像素级标注。此外，我们利用光流作为先验知识来有效训练模型。多项实验验证了我们方法的可行性与有效性。