AUTHOR=Avsar Ercan , Feekings Jordan P. , Krag Ludvig Ahm TITLE=Estimating catch rates in real time: Development of a deep learning based Nephrops (Nephrops norvegicus) counter for demersal trawl fisheries JOURNAL=Frontiers in Marine Science VOLUME=10 YEAR=2023 URL=https://www.frontiersin.org/journals/marine-science/articles/10.3389/fmars.2023.1129852 DOI=10.3389/fmars.2023.1129852 ISSN=2296-7745 ABSTRACT=
Demersal trawling is largely a blind process where information on catch rates and compositions is only available once the catch is taken onboard the vessel. Obtaining quantitative information on catch rates of target species while fishing can improve a fisheries economic and environmental performance as fishers would be able to use this information to make informed decisions during fishing. Despite there are real-time underwater monitoring systems developed for this purpose, the video data produced by these systems is not analyzed in near real-time. In other words, the user is expected to watch the video feed continuously to evaluate catch rates and composition. This is obviously a demanding process in which quantification of the fish counts will be of a qualitative nature. In this study, underwater footages collected using an in-trawl video recording system were processed to detect, track, and count the number of individuals of the target species,