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| Main Authors: | , , , |
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| Format: | Preprint |
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2022
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2206.09885 |
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| _version_ | 1866909331818020864 |
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| author | Nanda, Abhilasha Cho, Sung Won Lee, Hyeopwoo Park, Jin Hyoung |
| author_facet | Nanda, Abhilasha Cho, Sung Won Lee, Hyeopwoo Park, Jin Hyoung |
| contents | Over the years, datasets have been developed for various object detection tasks. Object detection in the maritime domain is essential for the safety and navigation of ships. However, there is still a lack of publicly available large-scale datasets in the maritime domain. To overcome this challenge, we present KOLOMVERSE, an open large-scale image dataset for object detection in the maritime domain by KRISO (Korea Research Institute of Ships and Ocean Engineering). We collected 5,845 hours of video data captured from 21 territorial waters of South Korea. Through an elaborate data quality assessment process, we gathered around 2,151,470 4K resolution images from the video data. This dataset considers various environments: weather, time, illumination, occlusion, viewpoint, background, wind speed, and visibility. The KOLOMVERSE consists of five classes (ship, buoy, fishnet buoy, lighthouse and wind farm) for maritime object detection. The dataset has images of 3840$\times$2160 pixels and to our knowledge, it is by far the largest publicly available dataset for object detection in the maritime domain. We performed object detection experiments and evaluated our dataset on several pre-trained state-of-the-art architectures to show the effectiveness and usefulness of our dataset. The dataset is available at: \url{https://github.com/MaritimeDataset/KOLOMVERSE}. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2206_09885 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | KOLOMVERSE: Korea open large-scale image dataset for object detection in the maritime universe Nanda, Abhilasha Cho, Sung Won Lee, Hyeopwoo Park, Jin Hyoung Computer Vision and Pattern Recognition Over the years, datasets have been developed for various object detection tasks. Object detection in the maritime domain is essential for the safety and navigation of ships. However, there is still a lack of publicly available large-scale datasets in the maritime domain. To overcome this challenge, we present KOLOMVERSE, an open large-scale image dataset for object detection in the maritime domain by KRISO (Korea Research Institute of Ships and Ocean Engineering). We collected 5,845 hours of video data captured from 21 territorial waters of South Korea. Through an elaborate data quality assessment process, we gathered around 2,151,470 4K resolution images from the video data. This dataset considers various environments: weather, time, illumination, occlusion, viewpoint, background, wind speed, and visibility. The KOLOMVERSE consists of five classes (ship, buoy, fishnet buoy, lighthouse and wind farm) for maritime object detection. The dataset has images of 3840$\times$2160 pixels and to our knowledge, it is by far the largest publicly available dataset for object detection in the maritime domain. We performed object detection experiments and evaluated our dataset on several pre-trained state-of-the-art architectures to show the effectiveness and usefulness of our dataset. The dataset is available at: \url{https://github.com/MaritimeDataset/KOLOMVERSE}. |
| title | KOLOMVERSE: Korea open large-scale image dataset for object detection in the maritime universe |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2206.09885 |