Antoni Burguera Burguera, Francisco Bonin-Font, Damianos Chatzievangelou, Maria Vigo Fernandez, Jacopo Aguzzi
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引用次数: 0
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.
期刊介绍：
The ICES Journal of Marine Science publishes original articles, opinion essays (“Food for Thought”), visions for the future (“Quo Vadimus”), and critical reviews that contribute to our scientific understanding of marine systems and the impact of human activities on them. The Journal also serves as a foundation for scientific advice across the broad spectrum of management and conservation issues related to the marine environment. Oceanography (e.g. productivity-determining processes), marine habitats, living resources, and related topics constitute the key elements of papers considered for publication. This includes economic, social, and public administration studies to the extent that they are directly related to management of the seas and are of general interest to marine scientists. Integrated studies that bridge gaps between traditional disciplines are particularly welcome.
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