Automatic Fish Population Counting by Machine Vision and a Hybrid Deep Neural Network Model

Simple Summary In aquaculture, the number of fish population can provide valuable input for the development of an intelligent production management system. Therefore, by using machine vision and a new hybrid deep neural network model, this paper proposes an automated fish population counting method to estimate the number of farmed Atlantic salmon. The experiment showed that the estimation accuracy can reach 95.06%, which can provide an essential reference for feeding and other breeding operations. Abstract In intensive aquaculture, the number of fish in a shoal can provide valuable input for the development of intelligent production management systems. However, the traditional artificial sampling method is not only time consuming and laborious, but also may put pressure on the fish. To solve the above problems, this paper proposes an automatic fish counting method based on a hybrid neural network model to realize the real-time, accurate, objective, and lossless counting of fish population in far offshore salmon mariculture. A multi-column convolution neural network (MCNN) is used as the front end to capture the feature information of different receptive fields. Convolution kernels of different sizes are used to adapt to the changes in angle, shape, and size caused by the motion of fish. Simultaneously, a wider and deeper dilated convolution neural network (DCNN) is used as the back end to reduce the loss of spatial structure information during network transmission. Finally, a hybrid neural network model is constructed. The experimental results show that the counting accuracy of the proposed hybrid neural network model is up to 95.06%, and the Pearson correlation coefficient between the estimation and the ground truth is 0.99. Compared with CNN- and MCNN-based methods, the accuracy and other evaluation indices are also improved. Therefore, the proposed method can provide an essential reference for feeding and other breeding operations.

[1]  Qihui Wu,et al.  A survey of machine learning for big data processing , 2016, EURASIP Journal on Advances in Signal Processing.

[2]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[3]  Lihong Xu,et al.  An Automated Fish Counting Algorithm in Aquaculture Based on Image Processing , 2017 .

[4]  Bin Li,et al.  Semantic Segmentation of Building Roof in Dense Urban Environment with Deep Convolutional Neural Network: A Case Study Using GF2 VHR Imagery in China , 2019, Sensors.

[5]  B. K. Liew,et al.  Automated Fish Counting Using Image Processing , 2009, 2009 International Conference on Computational Intelligence and Software Engineering.

[6]  Iasonas Kokkinos,et al.  DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[7]  Christian S. Perone,et al.  Spinal cord gray matter segmentation using deep dilated convolutions , 2017, Scientific Reports.

[8]  Xinting Yang,et al.  Evaluation of fish feeding intensity in aquaculture using a convolutional neural network and machine vision , 2019, Aquaculture.

[9]  Andrew Zisserman,et al.  Learning To Count Objects in Images , 2010, NIPS.

[10]  Nuno Vasconcelos,et al.  Bayesian Poisson regression for crowd counting , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[11]  Daoliang Li,et al.  Nonintrusive methods for biomass estimation in aquaculture with emphasis on fish: a review , 2020, Reviews in Aquaculture.

[12]  Xinghao Ding,et al.  Two-step approach for single underwater image enhancement , 2017, 2017 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS).

[13]  G. Buchsbaum.  A spatial processor model for object colour perception , 1980 .

[14]  L. Stien,et al.  Environmental drivers of Atlantic salmon behaviour in sea-cages: A review , 2011 .

[15]  J. N. Fabic,et al.  Fish population estimation and species classification from underwater video sequences using blob counting and shape analysis , 2013, 2013 IEEE International Underwater Technology Symposium (UT).

[16]  Daming Xu,et al.  Near-infrared imaging to quantify the feeding behavior of fish in aquaculture , 2017, Comput. Electron. Agric..

[17]  Jan Urban,et al.  Application of machine vision systems in aquaculture with emphasis on fish: state-of-the-art and key issues , 2017 .

[18]  Shenghua Gao,et al.  Single-Image Crowd Counting via Multi-Column Convolutional Neural Network , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[19]  Menglong Yan,et al.  Automatic Ship Detection in Remote Sensing Images from Google Earth of Complex Scenes Based on Multiscale Rotation Dense Feature Pyramid Networks , 2018, Remote. Sens..

[20]  Pavel Soucek,et al.  Comparative Performance Analysis of Support Vector Machine, Random Forest, Logistic Regression and k-Nearest Neighbours in Rainbow Trout (Oncorhynchus Mykiss) Classification Using Image-Based Features , 2018, Sensors.

[21]  Ariadne Barbosa Gonçalves,et al.  Automatic live fingerlings counting using computer vision , 2019, Comput. Electron. Agric..

[22]  C. M. Changa,et al.  Development of an intelligent feeding controller for indoor intensive culturing of eel , 2004 .

[23]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[24]  Dongjian He,et al.  FLYOLOv3 deep learning for key parts of dairy cow body detection , 2019, Comput. Electron. Agric..

[25]  Qiang Yang,et al.  A Survey on Transfer Learning , 2010, IEEE Transactions on Knowledge and Data Engineering.

[26]  Marco Willi,et al.  Identifying animal species in camera trap images using deep learning and citizen science , 2018, Methods in Ecology and Evolution.

[27]  Trevor Darrell,et al.  Fully Convolutional Networks for Semantic Segmentation , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[28]  Pietro Perona,et al.  Pedestrian Detection: An Evaluation of the State of the Art , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[29]  Y. Liu,et al.  Automate fry counting using computer vision and multi-class least squares support vector machine , 2013 .

[30]  Qingxiang Wu,et al.  Image super-resolution using a dilated convolutional neural network , 2018, Neurocomputing.

[31]  A. Causo,et al.  Automated fish fry counting and schooling behavior analysis using computer vision , 2012, 2012 IEEE 8th International Colloquium on Signal Processing and its Applications.

[32]  Anna Zamansky,et al.  Analysis of Dogs' Sleep Patterns Using Convolutional Neural Networks , 2019, ICANN.

[33]  Agnar Aamodt,et al.  A spatio-temporal recurrent network for salmon feeding action recognition from underwater videos in aquaculture , 2019, Comput. Electron. Agric..

[34]  Domenec Puig,et al.  A practical approach for detection and classification of traffic signs using Convolutional Neural Networks , 2016, Robotics Auton. Syst..

[35]  Syed Jawad Hussain Shah,et al.  Visual features based automated identification of fish species using deep convolutional neural networks , 2019, Comput. Electron. Agric..

[36]  Yun Shi,et al.  Cucumber leaf disease identification with global pooling dilated convolutional neural network , 2019, Comput. Electron. Agric..

[37]  Patrik Kamencay,et al.  Animal recognition system based on convolutional neural network , 2017 .

[38]  Francesco Visin,et al.  A guide to convolution arithmetic for deep learning , 2016, ArXiv.

[40]  Khawar Khurshid,et al.  Automatic fish detection in underwater videos by a deep neural network-based hybrid motion learning system , 2019, ICES Journal of Marine Science.

[41]  Lan Chen,et al.  Near infrared computer vision and neuro-fuzzy model-based feeding decision system for fish in aquaculture , 2018, Comput. Electron. Agric..

[42]  Raul Morais,et al.  Deep Learning Techniques for Grape Plant Species Identification in Natural Images , 2019, Sensors.