A Joint Application of Fuzzy Logic Approximation and a Deep Learning Neural Network to Build Fish Concentration Maps Based on Sonar Data

Abstract

This paper proposes an effective method for obtain topographic lake map with fish concentration based on the results of an intelligent sonar data processing. Fuzzy logic special implementation for approximation of sonar data is used. The mathematics apparatus of fuzzy logic provides the possibility of flexible adjustment approximator under conditions of problem to be solved when working with data of high dimensionality. An algorithm for obtaining fish concentration maps based on the results of intelligent processing of the sonar data is also proposed. The algorithm is based on the following steps: input frame separation into overlapping blocks, blocks-processing using convolutional neural networks YOLO v2, and merging extracted bounding boxes around one object. Experimental results for fish detection and fish concentrations map building are presented.