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

18 pages, 10 figures, 7 tables.-- Data availability: The data underlying this article were provided and are property by Institut de Ciències del Mar (ICM-CSIC). Data can be shared on request to the corresponding owner (ICM). plain-reversed

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

This work is partially supported by “ERDF A way of making Europe”, by Grant PLEC2021-007525/AEI/10.13039/501100011033 funded by the Agencia Estatal de Investigación, under Next Generation EU/PRTR. Also, authors which to thank other projects by the Plan Estatal de Investigación Científica y Técnica y de Innovación 2017–2020 of the Spanish government as support for the present work: BITER-LANDER (PID2020-114732RB-C32); BITER-ECO (PID2020-114732RB-C31); BITER-AUV (PID2020-114732RB-C33); PLOME (PLEC2021-007525/AEI/10.13039/501100011033). Moreover, part of the conceptual development, falls within the framework of EU LIFE Project ECOREST (LIFE20 NAT/ES/001270). DC was funded by a Juan de la Cierva Formación Postdoctoral Fellowship (Ref: FJC2021-047734-I; financed by MCIN/AEI/10.13039/501100011033 and European Union NextGenerationEU/PRTR funds). [...] The present research was carried out within the framework of the activities of the Spanish Government through the “Severo Ochoa Centre Excellence” accreditation to ICM-CSIC (CEX2019-000928-S) and the Research Unit Tecnoterra (ICM-CSIC/UPC)

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