Automatic Detection of Nephrops Norvegicus Burrows from Underwater Imagery Using Deep Learning
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AuthorsNaseer, A.; Nava-Baro, E.; Khan, S.D.; Vila, Y. (Yolanda); Doyle, J.
KeywordsFaster RCNN; computer vision; Nephrops norvegicus; nephrops norvegicus stock assessment; underwater videos classification
The Norway lobster, Nephrops norvegicus, is one of the main commercial crustacean fisheries in Europe. The abundance of Nephrops norvegicus stocks is assessed based on identifying and counting the burrows where they live from underwater videos collected by camera systems mounted on sledges. The Spanish Oceanographic Institute (IEO) andMarine Institute Ireland (MIIreland) conducts annual underwater television surveys (UWTV) to estimate the total abundance of Nephrops within the specified area, with a coefficient of variation (CV) or relative standard error of less than 20%. Currently, the identification and counting of the Nephrops burrows are carried out manually by the marine experts. This is quite a time-consuming job. As a solution, we propose an automated system based on deep neural networks that automatically detects and counts the Nephrops burrows in video footage with high precision. The proposed system introduces a deep-learning-based automated way to identify and ...
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