An automated computer vision system is proposed to perform taxonomic classification and fish size estimation from images taken with a low-cost digital camera. The system utilizes object detection, segmentation, and machine learning models trained on a dataset of 50,000 hand-annotated images containing 163 different fish species. By achieving high accuracy in fish segmentation, species classification, and length estimation tasks, the system offers a cost-effective solution for fish stock assessment at scale. The methodology combines citizen science with machine learning to reduce the cost of fisheries stock assessment significantly.
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by Moseli Mots'... klo arxiv.org 03-19-2024
https://arxiv.org/pdf/2403.10916.pdfSyvällisempiä Kysymyksiä