PoLaRIS Dataset: A Maritime Object Detection and Tracking Dataset in Pohang Canal
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arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866910751894011904 |
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| author | Choi, Jiwon Cho, Dongjin Lee, Gihyeon Kim, Hogyun Yang, Geonmo Kim, Joowan Cho, Younggun |
| author_facet | Choi, Jiwon Cho, Dongjin Lee, Gihyeon Kim, Hogyun Yang, Geonmo Kim, Joowan Cho, Younggun |
| contents | 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$10 pixels, which are crucial for maritime safety. To validate the dataset's effectiveness as a reliable benchmark, we conducted evaluations using various methodologies, including \ac{SOTA} techniques for object detection and tracking. These evaluations are expected to contribute to performance improvements, particularly in the complex maritime environment. To the best of our knowledge, this is the first dataset offering multi-modal annotations specifically tailored to maritime environments. Our dataset is available at https://sites.google.com/view/polaris-dataset. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_06192 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | PoLaRIS Dataset: A Maritime Object Detection and Tracking Dataset in Pohang Canal Choi, Jiwon Cho, Dongjin Lee, Gihyeon Kim, Hogyun Yang, Geonmo Kim, Joowan Cho, Younggun Robotics Maritime environments often present hazardous situations due to factors such as moving ships or buoys, which become obstacles under the influence of waves. In such challenging conditions, the ability to detect and track potentially hazardous objects is critical for the safe navigation of marine robots. To address the scarcity of comprehensive datasets capturing these dynamic scenarios, we introduce a new multi-modal dataset that includes image and point-wise annotations of maritime hazards. Our dataset provides detailed ground truth for obstacle detection and tracking, including objects as small as 10$\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. |
| title | PoLaRIS Dataset: A Maritime Object Detection and Tracking Dataset in Pohang Canal |
| topic | Robotics |
| url | https://arxiv.org/abs/2412.06192 |