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

Abstract

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.