Proceedings of the International Conference on Digital Technologies in Logistics and Infrastructure (ICDTLI 2019)

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

Authors
J. Mäkiö, D. Glukhov, R. Bohush, T. Hlukhava, I. Zakharava
Corresponding Author
J. Mäkiö
Available Online undefined NaN.
DOI
https://doi.org/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.
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Proceedings
International Conference on Digital Technologies in Logistics and Infrastructure (ICDTLI 2019)
Publication Date
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ISBN
978-94-6252-799-7
DOI
https://doi.org/10.2991/icdtli-19.2019.84How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

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