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

DOI

10.2991/icdtli-19.2019.84How to use a DOI?

Keywords

sonar data; fish concentration; maps of lakes; fuzzy logic; convolutional neural networks

Abstract

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.

Copyright

© 2019, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - J. Mäkiö
AU  - D. Glukhov
AU  - R. Bohush
AU  - T. Hlukhava
AU  - I. Zakharava
PY  - 2019/09
DA  - 2019/09
TI  - Fuzzy Logic Approximation and Deep Learning Neural Network for Fish Concentration Maps
BT  - Proceedings of the International Conference on Digital Technologies in Logistics and Infrastructure (ICDTLI 2019)
PB  - Atlantis Press
SP  - 479
EP  - 484
SN  - 2589-4900
UR  - https://doi.org/10.2991/icdtli-19.2019.84
DO  - 10.2991/icdtli-19.2019.84
ID  - Mäkiö2019/09
ER  -