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.
To Another Language
from source content
arxiv.org
Principais Insights Extraídos De
by Moseli Mots'... às arxiv.org 03-19-2024
https://arxiv.org/pdf/2403.10916.pdfPerguntas Mais Profundas