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

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.

[1]  Ali Farhadi,et al.  YOLO9000: Better, Faster, Stronger , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[2]  Ali Farhadi,et al.  You Only Look Once: Unified, Real-Time Object Detection , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[3]  Sergey Ioffe,et al.  Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.

[4]  Kaiming He,et al.  Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[6]  Sergey Ioffe,et al.  Rethinking the Inception Architecture for Computer Vision , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[7]  Peter Waszkewitz,et al.  Industrial Image Processing: Visual Quality Control in Manufacturing , 1999 .

[8]  Huafeng Chen,et al.  An effective algorithm to detect both smoke and flame using color and wavelet analysis , 2017, Pattern Recognition and Image Analysis.

[9]  Son-Cheol Yu,et al.  Convolutional neural network-based real-time ROV detection using forward-looking sonar image , 2016, 2016 IEEE/OES Autonomous Underwater Vehicles (AUV).

[10]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[11]  Helge Balk,et al.  Improved fish detection in data from split-beam sonar , 2000 .