Deep neural networks for analysis of fisheries surveillance video and automated monitoring of fish discards
Year: 2019
Type: article
Abstract: We report on the development of a computer vision system that analyses video from CCTV systems installed on fishing trawlers for the purpose of monitoring and quantifying discarded fish catch. Our system is designed to operate in spite of the challenging computer vision problem posed by conditions on-board fishing trawlers. We describe the approaches developed for isolating and segmenting individual fish and for species classification. We present an analysis of the variability of manual species identification performed by expert human observers and contrast the performance of our species classifier against this benchmark. We also quantify the effect of the domain gap on the performance of modern deep neural network-based computer vision systems.
Source: ICES Journal of Marine Science
Authors: Geoff French, Michal Mackiewicz, Mark Fisher, Helen Holah, Rachel Kilburn, Neil Campbell, Coby Needle
Institutions: University of East Anglia, Norwich Research Park
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
SDG: Life below water
Open Access status: bronze
OpenAlex ID: W2964583519