Visual features based automated identification of fish species using deep convolutional neural networks

Abstract Morphological based fish species identification is an erroneous and time-consuming process. There are numerous fish species and due to their close resemblance with each other, it is difficult to classify them by external characters. Recently, computer vision and deep learning-based identification of different animal species is being widely used by the researchers. Convolutional Neural Network (CNN) is one of the most analytically powerful tools in deep learning architecture for the image classification based on visual features. This work aims to propose a deep learning framework based on the CNN method for fish species identification. The proposed CNN architecture contains 32 deep layers that are considerably deep to derive valuable and discriminating features from the image. The deep supervision is inflicted on the VGGNet architecture to increase the classification performance by instantly adding four convolutional layers to the training of each level in the network. To test the performance of proposed 32-Layer CNN architecture, we developed a dataset termed as Fish-Pak and is publicly available at Mendeley data (Fish-Pak: https://doi.org/10.17632/n3ydw29sbz.3#folder-6b024354-bae3-460a-a758-352685ba0e38 ). Fish-Pak contains 915 images with six distinct classes; Ctenopharyngodon idella (Grass carp), Cyprinus carpio (Common carp), Cirrhinus mrigala (Mori), Labeo rohita (Rohu), Hypophthalmichthys molitrix (Silver carp), and Catla catla (Thala) and three different image views (head region, body shape, and scale). To ensure the superior performance of proposed CNN architecture, we have carried out the experimental comparison with other deep learning frameworks involving VGG-16 for transfer learning, one block VGG, two block VGG, three block VGG, LeNet-5, AlexNet, GoogleNet, and ResNet-50 on the Fish-Pak data set. Comprehensive empirical analyses reveal that the proposed method achieves state of the art performance and outperforms existing methods.

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

[2]  U. Rajendra Acharya,et al.  Segmentation of optic disc, fovea and retinal vasculature using a single convolutional neural network , 2017, J. Comput. Sci..

[3]  Mohammed Bennamoun,et al.  Deep Learning for Marine Species Recognition , 2019, Handbook of Deep Learning Applications.

[4]  Yoshua Bengio,et al.  Gradient-based learning applied to document recognition , 1998, Proc. IEEE.

[5]  Han Zhongzhi.  Computer Vision-Based Agriculture Engineering , 2019 .

[6]  Morten Goodwin Olsen,et al.  Biometric Fish Classification of Temperate Species Using Convolutional Neural Network with Squeeze-and-Excitation , 2019, IEA/AIE.

[7]  Daoliang Li,et al.  Fish species classification by color, texture and multi-class support vector machine using computer vision , 2012 .

[8]  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.

[9]  Ajmal Mian,et al.  Fish species classification in unconstrained underwater environments based on deep learning , 2016 .

[10]  Joel J. P. C. Rodrigues,et al.  A novel deep learning based framework for the detection and classification of breast cancer using transfer learning , 2019, Pattern Recognit. Lett..

[11]  Shahrul Azman Mohd Noah,et al.  Fish Classification Based on Robust Features Extraction From Color Signature Using Back-Propagation Classifier , 2011 .

[12]  Yan-Fu Kuo,et al.  Identifying the species of harvested tuna and billfish using deep convolutional neural networks , 2020 .

[13]  Zhiqiang Que,et al.  An efficient convolutional neural network for small traffic sign detection , 2019, J. Syst. Archit..

[14]  C. Chandrasekar,et al.  A Comparison of various Edge Detection Techniques used in Image Processing , 2012 .

[15]  Sushant Jain,et al.  Underwater Fish Species Classification using Convolutional Neural Network and Deep Learning , 2017, 2017 Ninth International Conference on Advances in Pattern Recognition (ICAPR).

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

[17]  Flávio L. C. Pádua,et al.  Evaluating cluster detection algorithms and feature extraction techniques in automatic classification of fish species , 2013, Pattern Analysis and Applications.

[18]  Dumitru Erhan,et al.  Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[19]  Ayten Atasoy,et al.  Study of fish species discrimination via electronic nose , 2015, Comput. Electron. Agric..

[20]  Ahmad Salman,et al.  Real-time fish detection in complex backgrounds using probabilistic background modelling , 2019, Ecol. Informatics.

[21]  Juan You,et al.  Classification of freshwater fish species by linear discriminant analysis based on near infrared reflectance spectroscopy , 2017 .

[22]  Wesley Nunes Gonçalves,et al.  Improving Pantanal fish species recognition through taxonomic ranks in convolutional neural networks , 2019, Ecol. Informatics.

[23]  Sven Behnke,et al.  Evaluation of Pooling Operations in Convolutional Architectures for Object Recognition , 2010, ICANN.

[24]  Bastiaan Star,et al.  Unlocking the potential of ancient fish DNA in the genomic era , 2019, Evolutionary applications.

[25]  Syed Jawad Hussain Shah,et al.  Fish-Pak: Fish species dataset from Pakistan for visual features based classification , 2019, Data in brief.

[26]  Tanaka Akinori,et al.  Detection of Phase Transition via Convolutional Neural Networks , 2016, 1609.09087.

[27]  T. Poggio,et al.  Hierarchical models of object recognition in cortex , 1999, Nature Neuroscience.

[28]  Nitish Srivastava,et al.  Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..

[29]  U. Rajendra Acharya,et al.  Automated detection of arrhythmias using different intervals of tachycardia ECG segments with convolutional neural network , 2017, Inf. Sci..

[30]  Unai Irusta,et al.  Deep Neural Networks for ECG-Based Pulse Detection during Out-of-Hospital Cardiac Arrest , 2019, Entropy.

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

[32]  Peter I. Corke,et al.  Modelling local deep convolutional neural network features to improve fine-grained image classification , 2015, 2015 IEEE International Conference on Image Processing (ICIP).

[33]  Phil F. Culverhouse,et al.  Automated identification of benthic epifauna with computer vision , 2019, Marine Ecology Progress Series.

[34]  Alfonso B. Labao,et al.  Cascaded deep network systems with linked ensemble components for underwater fish detection in the wild , 2019, Ecol. Informatics.

[35]  Muhammad Imran Malik,et al.  Automatic fish species classification in underwater videos: exploiting pre-trained deep neural network models to compensate for limited labelled data , 2018 .