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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2501.03767 |
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| _version_ | 1866910775769038848 |
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| author | Bengtson, Stefan Hein Lehotský, Daniel Ismiroglou, Vasiliki Madsen, Niels Moeslund, Thomas B. Pedersen, Malte |
| author_facet | Bengtson, Stefan Hein Lehotský, Daniel Ismiroglou, Vasiliki Madsen, Niels Moeslund, Thomas B. Pedersen, Malte |
| contents | Automated fish documentation processes are in the near future expected to play an essential role in sustainable fisheries management and for addressing challenges of overfishing. In this paper, we present a novel and publicly available dataset named AutoFish designed for fine-grained fish analysis. The dataset comprises 1,500 images of 454 specimens of visually similar fish placed in various constellations on a white conveyor belt and annotated with instance segmentation masks, IDs, and length measurements. The data was collected in a controlled environment using an RGB camera. The annotation procedure involved manual point annotations, initial segmentation masks proposed by the Segment Anything Model (SAM), and subsequent manual correction of the masks. We establish baseline instance segmentation results using two variations of the Mask2Former architecture, with the best performing model reaching an mAP of 89.15%. Additionally, we present two baseline length estimation methods, the best performing being a custom MobileNetV2-based regression model reaching an MAE of 0.62cm in images with no occlusion and 1.38cm in images with occlusion. Link to project page: https://vap.aau.dk/autofish/. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_03767 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | AutoFish: Dataset and Benchmark for Fine-grained Analysis of Fish Bengtson, Stefan Hein Lehotský, Daniel Ismiroglou, Vasiliki Madsen, Niels Moeslund, Thomas B. Pedersen, Malte Computer Vision and Pattern Recognition Automated fish documentation processes are in the near future expected to play an essential role in sustainable fisheries management and for addressing challenges of overfishing. In this paper, we present a novel and publicly available dataset named AutoFish designed for fine-grained fish analysis. The dataset comprises 1,500 images of 454 specimens of visually similar fish placed in various constellations on a white conveyor belt and annotated with instance segmentation masks, IDs, and length measurements. The data was collected in a controlled environment using an RGB camera. The annotation procedure involved manual point annotations, initial segmentation masks proposed by the Segment Anything Model (SAM), and subsequent manual correction of the masks. We establish baseline instance segmentation results using two variations of the Mask2Former architecture, with the best performing model reaching an mAP of 89.15%. Additionally, we present two baseline length estimation methods, the best performing being a custom MobileNetV2-based regression model reaching an MAE of 0.62cm in images with no occlusion and 1.38cm in images with occlusion. Link to project page: https://vap.aau.dk/autofish/. |
| title | AutoFish: Dataset and Benchmark for Fine-grained Analysis of Fish |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2501.03767 |