用合成数据提升铁路障碍物检测，覆盖多种天气与地形。
SynRailObs: A Synthetic Dataset for Obstacle Detection in Railway Scenarios
- 用扩散模型生成稀有难捕障碍物，增强数据多样性。
- 模型在真实轨道上跨距离、跨天气表现稳定。
- 零样本能力适合高安全要求场景，如铁路安防。
铁路环境中的障碍物检测对预防重大事故至关重要。在复杂条件下识别多种障碍类别，需大规模、高质量标注的图像数据集。然而现有公开数据集难以满足需求，制约了铁路安全研究进展。为此，我们提出高保真合成数据集 SynRailObs，涵盖多样天气条件与地理特征。同时利用扩散模型生成现实中罕见且难采集的障碍物。为验证其有效性，我们在真实铁路场景中测试，涵盖有砟与无砟轨道，以及多种天气条件。结果表明，基于 SynRailObs 训练的模型在不同距离和环境条件下均表现一致，且具备零样本能力，对安全敏感领域应用具有重要意义。数据已公开于 https://www.kaggle.com/datasets/qiushi910/synrailobs。
原文摘要 · Abstract (English)
Detecting potential obstacles in railway environments is critical for preventing serious accidents. Identifying a broad range of obstacle categories under complex conditions requires large-scale datasets with precisely annotated, high-quality images. However, existing publicly available datasets fail to meet these requirements, thereby hindering progress in railway safety research. To address this gap, we introduce SynRailObs, a high-fidelity synthetic dataset designed to represent a diverse range of weather conditions and geographical features. Furthermore, diffusion models are employed to generate rare and difficult-to-capture obstacles that are typically challenging to obtain in real-world scenarios. To evaluate the effectiveness of SynRailObs, we perform experiments in real-world railway environments, testing on both ballasted and ballastless tracks across various weather conditions. The results demonstrate that SynRailObs holds substantial potential for advancing obstacle detection in railway safety applications. Models trained on this dataset show consistent performance across different distances and environmental conditions. Moreover, the model trained on SynRailObs exhibits zero-shot capabilities, which are essential for applications in security-sensitive domains. The data is available in https://www.kaggle.com/datasets/qiushi910/synrailobs.
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。