核心概念
Automated computer vision system for fish stock assessment using deep neural networks.
要約
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.
統計
Our system achieves a 92% intersection over union on the fish segmentation task.
It attains an 89% top-1 classification accuracy on single fish species classification.
The mean error on the fish length estimation task is 2.3 cm.
引用
"Advances in digital photography, computer vision, and artificial intelligence make automating fish-stock estimation an attractive alternative."
"We propose a methodology for drastically reducing the cost of fisheries stock assessment by combining citizen science with machine learning."
"Our system achieves high accuracy in fish segmentation, species classification, and length estimation tasks."