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.
| 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 |