Automatic detection, identification and counting of deep-water snappers on underwater baited video using deep learning

Frontiers in Marine Science, Journal Year: 2025, Volume and Issue: 12

Published: Feb. 6, 2025

Deep-sea demersal fisheries in the Pacific have strong commercial, cultural, and recreational value, especially snappers (Lutjanidae) which make bulk of catches. Yet, managing these is challenging due to scarcity data. Stereo-Baited Remote Underwater Video Stations (BRUVS) can provide valuable quantitative information on fish stocks, but manually processing large amounts videos time-consuming sometimes unrealistic. To address this issue, we used a Region-based Convolutional Neural Network (Faster R-CNN), deep learning architecture automatically detect, identify count deep-water BRUVS. Videos were collected New Caledonia (South Pacific) at depths ranging from 47 552 m. Using dataset 12,100 annotations 11 snapper species observed 6,364 images, obtained good model performance for 6 with sufficient (F-measures >0.7, up 0.87). The correlation between automatic manual estimates MaxN abundance was high (0.72 – 0.9), Faster R-CNN showed an underestimation bias higher abundances. A semi-automatic protocol where our supported observers BRUVS footage improved 0.96 counts perfect match (R=1) some key species. This already assist semi-automatically process will certainly improve when more training data be available decrease rate false negatives. study further shows that use artificial intelligence marine science progressive warranted future.

Language: Английский

Leveraging VGG16 for Fish Classification in a Large-Scale Dataset

Brilliance Research of Artificial Intelligence, Journal Year: 2023, Volume and Issue: 3(2), P. 316 - 328

Published: Dec. 15, 2023

When the VGG16 model was applied to fish picture classification, overall accuracy a remarkable 99%, demonstrating strong performance over most of dataset. Still, thorough assessment model's efficacy necessitates look beyond its general accuracy. A more detailed evaluation is possible thanks class-specific metrics like precision, recall, and F1-score, which provide information on how well performs particular classes. Although high encouraging, research into these possibility class imbalances should be taken account guarantee consistent in image classification challenge across all categories. comprehensive effectiveness benefits from contextual knowledge application domain careful examination measures.

Language: Английский