Deep learning for detection and counting of Nephrops norvegicus from underwater videos

Abstract The Norway lobster (Nephrops norvegicus) is one of the most important fishery items for the EU blue economy. This paper describes a software architecture based on neural networks, designed to identify the presence of N. norvegicus and estimate the number of its individuals per square meter...

Full description

Saved in:
Bibliographic Details
Published in:ICES journal of marine science 2024-09, Vol.81 (7), p.1307-1324
Main Authors: Burguera, Antoni Burguera, Bonin-Font, Francisco, Chatzievangelou, Damianos, Fernandez, Maria Vigo, Aguzzi, Jacopo
Format: Article
Language:English
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Abstract The Norway lobster (Nephrops norvegicus) is one of the most important fishery items for the EU blue economy. This paper describes a software architecture based on neural networks, designed to identify the presence of N. norvegicus and estimate the number of its individuals per square meter (i.e. stock density) in deep-sea (350–380 m depth) Fishery No-Take Zones of the northwestern Mediterranean. Inferencing models were obtained by training open-source networks with images obtained from frames partitioning of in submarine vehicle videos. Animal detections were also tracked in successive frames of video sequences to avoid biases in individual recounting, offering significant success and precision in detection and density estimations.
ISSN:1054-3139
1095-9289
DOI:10.1093/icesjms/fsae089