@inproceedings{M{\"a}ki{\"o}GlukhovBohushetal.2019,
  author    = {M{\"a}ki{\"o}, Juho and Glukhov, D. and Bohush, R. and Hlukhava, T.},
  title     = {Fuzzy Logic Approximation and Deep Learning Neural Network for Fish Concentration Maps},
  booktitle = {Atlantis Highlights in Computer Sciences; International Conference on Digital Technologies in Logistics and Infrastructure (ICDTLI), 04.04.-05.04.2019, St. Petersburg (Russia)},
  volume    = {1},
  isbn      = {978-94-6252-799-7},
  issn      = {2589-4900},
  doi       = {10.2991/icdtli-19.2019.84},
  institution = {Fachbereich Technik},
  pages     = {484 -- 488},
  year      = {2019},
  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.},
  language  = {en}
}