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Fuzzy Logic Approximation and Deep Learning Neural Network for Fish Concentration Maps

  • This paper proposes an algorithm to obtain topographic maps of lakes, maps of fish concentration and a map of predator location based on the results of an intelligent sonar data processing. The algorithm is based on the following steps: input frame separation into overlapping blocks, blocks-processing using convolutional neural networks (CNN) YOLO v2, and merging extracted bounding boxes around one object. To construct maps of the distribution of features along the lake, we propose a novel method for constructing the approximation of GPS- referenced CNN results based on the original implementation of fuzzy logic.

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Metadaten
Author:Juho MäkiöORCiD, D. Glukhov, R. Bohush, T. Hlukhava
DOI:https://doi.org/10.2991/icdtli-19.2019.84
ISBN:978-94-6252-799-7
ISSN:2589-4900
Parent Title (English):Atlantis Highlights in Computer Sciences; International Conference on Digital Technologies in Logistics and Infrastructure (ICDTLI), 04.04.-05.04.2019, St. Petersburg (Russia)
Document Type:Conference Proceeding
Language:English
Year of Completion:2019
Release Date:2025/06/02
Tag:Convolutional neural networks; Fish concentration; Fuzzy logic; Maps of lakes; Sonar data
Volume:1
First Page:484
Last Page:488
Institute:Fachbereich Technik
Research Focus Area:Industrielle Informatik