FishNet: Deep Neural Networks for Low-Cost Fish Stock Estimation
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866910505236430848 |
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| author | Mots'oehli, Moseli Nikolaev, Anton IGede, Wawan B. Lynham, John Mous, Peter J. Sadowski, Peter |
| author_facet | Mots'oehli, Moseli Nikolaev, Anton IGede, Wawan B. Lynham, John Mous, Peter J. Sadowski, Peter |
| contents | Fish stock assessment often involves manual fish counting by taxonomy specialists, which is both time-consuming and costly. We propose FishNet, an automated computer vision system for both taxonomic classification and fish size estimation from images captured with a low-cost digital camera. The system first performs object detection and segmentation using a Mask R-CNN to identify individual fish from images containing multiple fish, possibly consisting of different species. Then each fish species is classified and the length is predicted using separate machine learning models. To develop the model, we use a dataset of 300,000 hand-labeled images containing 1.2M fish of 163 different species and ranging in length from 10cm to 250cm, with additional annotations and quality control methods used to curate high-quality training data. On held-out test data sets, our system achieves a 92% intersection over union on the fish segmentation task, a 89% top-1 classification accuracy on single fish species classification, and a 2.3cm mean absolute error on the fish length estimation task. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2403_10916 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | FishNet: Deep Neural Networks for Low-Cost Fish Stock Estimation Mots'oehli, Moseli Nikolaev, Anton IGede, Wawan B. Lynham, John Mous, Peter J. Sadowski, Peter Computer Vision and Pattern Recognition General Economics Economics Fish stock assessment often involves manual fish counting by taxonomy specialists, which is both time-consuming and costly. We propose FishNet, an automated computer vision system for both taxonomic classification and fish size estimation from images captured with a low-cost digital camera. The system first performs object detection and segmentation using a Mask R-CNN to identify individual fish from images containing multiple fish, possibly consisting of different species. Then each fish species is classified and the length is predicted using separate machine learning models. To develop the model, we use a dataset of 300,000 hand-labeled images containing 1.2M fish of 163 different species and ranging in length from 10cm to 250cm, with additional annotations and quality control methods used to curate high-quality training data. On held-out test data sets, our system achieves a 92% intersection over union on the fish segmentation task, a 89% top-1 classification accuracy on single fish species classification, and a 2.3cm mean absolute error on the fish length estimation task. |
| title | FishNet: Deep Neural Networks for Low-Cost Fish Stock Estimation |
| topic | Computer Vision and Pattern Recognition General Economics Economics |
| url | https://arxiv.org/abs/2403.10916 |