Maritime environments often present hazardous situations due to factors such as moving ships or buoys, which become obstacles under the influence of waves. In such challenging conditions, the ability to detect and track potentially hazardous objects is critical for the safe navigation of marine robots. To address the scarcity of comprehensive datasets capturing these dynamic scenarios, we introduce a new multi-modal dataset that includes image and point-wise annotations of maritime hazards. Our dataset provides detailed ground truth for obstacle detection and tracking, including objects as small as 10$\times$10 pixels, which are crucial for maritime safety. To validate the dataset's effectiveness as a reliable benchmark, we conducted evaluations using various methodologies, including \ac{SOTA} techniques for object detection and tracking. These evaluations are expected to contribute to performance improvements, particularly in the complex maritime environment. To the best of our knowledge, this is the first dataset offering multi-modal annotations specifically tailored to maritime environments. Our dataset is available at https://sites.google.com/view/polaris-dataset.

翻译：海上环境常因移动船只或浮标等因素构成危险情境，这些物体在波浪影响下会成为航行障碍。在此类挑战性条件下，检测和跟踪潜在危险目标的能力对海洋机器人的安全航行至关重要。为弥补现有数据集在捕捉此类动态场景方面的不足，我们提出了一个包含海上危险目标图像与逐点标注的新型多模态数据集。本数据集为障碍物检测与跟踪提供了精细的真实标注，包含小至10×10像素的关键目标，这对海上安全具有重要意义。为验证数据集作为可靠基准的有效性，我们采用多种方法进行评估，包括目标检测与跟踪的\ac{SOTA}技术。这些评估有望推动性能提升，特别是在复杂的海上环境中。据我们所知，这是首个专门针对海上环境设计的多模态标注数据集。本数据集可通过https://sites.google.com/view/polaris-dataset获取。