The progress in maritime obstacle detection is hindered by the lack of a diverse dataset that adequately captures the complexity of general maritime environments. We present the first maritime panoptic obstacle detection benchmark LaRS, featuring scenes from Lakes, Rivers and Seas. Our major contribution is the new dataset, which boasts the largest diversity in recording locations, scene types, obstacle classes, and acquisition conditions among the related datasets. LaRS is composed of over 4000 per-pixel labeled key frames with nine preceding frames to allow utilization of the temporal texture, amounting to over 40k frames. Each key frame is annotated with 8 thing, 3 stuff classes and 19 global scene attributes. We report the results of 27 semantic and panoptic segmentation methods, along with several performance insights and future research directions. To enable objective evaluation, we have implemented an online evaluation server. The LaRS dataset, evaluation toolkit and benchmark are publicly available at: https://lojzezust.github.io/lars-dataset

翻译：海洋障碍物检测的进展受到缺乏能够充分捕捉一般海洋环境复杂性的多样化数据集的阻碍。我们提出了首个海洋全景障碍物检测基准LaRS，涵盖湖泊、河流和海洋场景。我们的主要贡献在于新数据集，它在记录位置、场景类型、障碍物类别和采集条件方面拥有相关数据集中最大的多样性。LaRS包含超过4000帧逐像素标注的关键帧，以及前序九帧以利用时序纹理，总计超过40,000帧。每帧关键帧标注了8个物体类、3个语义类和19个全局场景属性。我们报告了27种语义分割和全景分割方法的结果，并提供了若干性能洞察及未来研究方向。为便于客观评估，我们实现了一个在线评估服务器。LaRS数据集、评估工具包和基准已在以下网址公开提供：https://lojzezust.github.io/lars-dataset