Deep learning–based detection of Nephrops norvegicus burrows

This work presents a methodology to support the assessment of a benthic species of great commercial importance (Nephrops norvegicus) taking advantage of a combination of machine learning and computer vision methods. Up to the present, abundance indices based on the density of this species are evaluated by visual inspection of underwater imagery and through manual counting of the observed burrows. A novel approach is proposed, based on the integration in the processing pipeline of a supervised learning model in charge of detecting the burrows. The model is trained exploiting underwater videos that experts annotate by identifying the frames where burrows are present and specifying the related features. To pursue such a goal, the proposed automated procedure must cope with several environmental issues, such as high underwater turbidity, uneven illumination, heavy colour distortions, as well as complexities arising from the presence of ambiguous objects and morphological features that may affect the misclassification rate. The proposed method was developed on video material collected in a specific area, but has the potential to be applied throughout the species' distribution range. Preliminary results concerning the analysis of data captured in the central Adriatic Sea are presented and discussed.