Fuzzy Logic Approximation and Deep Learning Neural Network for Fish Concentration Maps
J. Mäkiö, D. Glukhov, R. Bohush, T. Hlukhava, I. Zakharava
Available Online September 2019.
- https://doi.org/10.2991/icdtli-19.2019.84How to use a DOI?
- sonar data; fish concentration; maps of lakes; fuzzy logic; convolutional neural networks
- 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.
- 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 - 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 - https://doi.org/10.2991/icdtli-19.2019.84 ID - Mäkiö2019/09 ER -