Abstract
Fish species classification in underwater images is an emerging research area for scientists and researchers in the field of image processing. Fish species classification in underwater images is an important task for fish survey i.e. to audit ecological balance, monitoring fish population and preserving endangered species. But the phenomenon of light scattering and absorption in ocean water leads to hazy, dull and low contrast images making fish classification a tedious and tough task. Convolutional Neural Networks (CNNs) can be the solution for fish species classification problem but the scarcity of ample fish images leads to the serious issue of training a neural network from scratch. To overcome the issue of limited dataset the present paper proposes a transfer learning based fish species classification method for underwater images. ResNet-50 network has been used for transfer learning as it reduces the vanishing gradient problem to minimum by using residual blocks and thus improving the accuracies. Training only last few layers of ResNet-50 network with transfer learning increases the classification accuracy despite of scarce dataset. The proposed method has been tested on two datasets comprising of 27, 370 (i.e. large dataset) and 600 images (i.e. small dataset) without any data augmentation. Experimental results depict that the proposed network achieves a validation accuracy of \(98.44\%\) for large dataset and \(84.92\%\) for smaller dataset. With the performance analysis, it is observed that this transfer learning based approach led to better results by providing high precision, recall and F1score values of 0.94, 0.85 and 0.89, respectively.
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References
Roberts PLD, Jaffe JS, Trivedi MM. Multiview, broadband acoustic classification of marine fish: a machine learning framework and comparative analysis. IEEE J Oceanic Eng. 2011;36(1):90–104.
Wang G, Hwang J, Williams K, Wallace F, Rose CS. Shrinking encoding with two-level codebook learning for fine-grained fish recognition. In: 2016 ICPR 2nd Workshop on Computer Vision for Analysis of Underwater Imagery (CVAUI). 2016; pages 31–36.
Li X, Shen H, Zhang L, Zhang H, Yuan Q, Yang G. Recovering quantitative remote sensing products contaminated by thick clouds and shadows using multitemporal dictionary learning. IEEE Trans Geosci Remote Sens. 2014;52(11):7086–98.
Schettini R, Corchs S. Underwater image processing: state of the art of restoration and image enhancement methods. EURASIP J Adv Signal Process. 2010;2010(1):1–14.
Hasija S, Buragohain MJ, Indu S. Fish species classification using graph embedding discriminant analysis. In: 2017 International Conference on Machine Vision and Information Technology (CMVIT). 2017; pages 81–86.
Sbragaglia V, Marini S, Fanelli E. Tracking fish abundance by underwater image recognition. Sci Rep. 2018;8:1–12.
Andrialovanirina N, Ponton D, Behivoke F, Mahafina J, Léopold M. A powerful method for measuring fish size of small-scale fishery catches using imagej. Fisheries Res. 2020;223:1–8.
Kuanar S, Rao KR, Bilas M. Adaptive cu mode selection in hevc intra prediction: a deep learning approach. Circuits Syst Signal Process. 2019;38:5081–102.
Kuanar S, Rao KR, Mahapatra D, Bilas M. Night time haze and glow removal using deep dilated convolutional network, 2019. arXiv:1902.00855.
Cabreira A, Tripode M, Madirolas A. Artificial neural networks for fish-species identification. ICES J Mar Sci. 2009;66:1–11 (06).
Spampinato C, Giordano D, Di Salvo R, Jessica YH, Burger C, Fisher RB, Nadarajan G. Automatic fish classification for underwater species behavior understanding. In: ARTEMIS@ACM Multimedia. 2010; pages 1–6.
Robotham H, Bosch P, Gutiérrez-Estrada JC, Castillo J, Pulido-Calvo I. Acoustic identification of small pelagic fish species in chile using support vector machines and neural networks. Fish Res. 2010;102(1):115–22.
Huang PX, Boom BJ, Fisher B. Hierarchical classification for live fish recognition, In: BMVC, 2012. pp 1–10.
Hu J, Li D, Duan Q, Han Y, Chen G, Si X. Fish species classification by color, texture and multi-class support vector machine using computer vision. Comput Electron Agric. 2012;88:133–40.
Fouad MMM, Zawbaa HM, El-Bendary N, Hassanien AE. Automatic nile tilapia fish classification approach using machine learning techniques. In: 13th International Conference on Hybrid Intelligent Systems (HIS 2013). 2013; pages 173–178.
Chuang M, Hwang J, Kuo F, Shan M, Williams K. Recognizing live fish species by hierarchical partial classification based on the exponential benefit. In. 2014 IEEE International Conference on Image Processing (ICIP). 2014; pages 5232–5236.
Ogunlana SO, Olabode O, Oluwadare SAA, Iwasokun GB. Fish classification using support vector machine. Afr J Comput ICTs. 2015;8:75–82.
Qin H, Li X, Yang Z, Shang M. When underwater imagery analysis meets deep learning: a solution at the age of big visual data. In: OCEANS 2015 - MTS/IEEE Washington. 2015; pages 1–5.
Rodrigues MTA, Freitas MHG, Pádua FLC, Gomes RM, Carrano EG, Eduardo G. Evaluating cluster detection algorithms and feature extraction techniques in automatic classification of fish species. Pattern Anal Appl. 2015;18(4):783–97.
Salman A, Jalal A, Shafait F, Mian A, Shortis M, Seager J, Harvey E. Fish species classification in unconstrained underwater environments based on deep learning. Limnol Oceanogr. 2016;14(9):570–85.
Liang J, Qin H, Li X, Zhang C. Deepfish: accurate underwater live fish recognition with a deep architecture. Neurocomputing. 2016;187:49–58.
Rathi D, Jain S, Indu S. Underwater fish species classification using convolutional neural network and deep learning. In: 2017 Ninth International Conference on Advances in Pattern Recognition (ICAPR). 2017; pages 1–6.
Demertzis K, Iliadis LS, Anezakis VD. Extreme deep learning in biosecurity: the case of machine hearing for marine species identification. J Inf Telecommun. 2018;2(4):492–510.
Hussain M, Ather I, Wang Z, Ali Z, Riaz S. Automatic fish species classification using deep convolutional neural networks. Wirel Pers Commun. 2019;116:1043–53 (08).
Chuang M, Hwang J, Williams K. A feature learning and object recognition framework for underwater fish images. IEEE Trans Image Process. 2016;25(4):1862–72.
Qiu C, Zhang S, Wang C, Yu Z, Zheng H, Zheng B. Improving transfer learning and squeeze- and-excitation networks for small-scale fine-grained fish image classification. IEEE Access. 2018;6:78503–12.
Putra D, Hridayami P, Gede IK, Wibawa KS. Fish species recognition using vgg16 deep convolutional neural network. J Comput Sci Eng. 2019;13(3):124–30.
Rawat W, Wang Z. Deep convolutional neural networks for image classification: a comprehensive review. Neural Comput. 2017;29:1–98 (06).
Canziani A, Paszke A, Culurciello E. An analysis of deep neural network models for practical applications, 2016. arXiv:1605.07678.
Deng J, Dong W, Socher R, Li L, Li K, Fei-Fei L. Imagenet: a large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition. 2009; pages 248–255.
Koustubh. https://cv-tricks.com/cnn/understand-resnet-alexnet-vgg-inception.
He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. 2016; pages 770–778, 06.
Boom BJ, Huang PX, He J, Fisher RB. Supporting ground-truth annotation of image datasets using clustering. In: Proceedings of the 21st International Conference on Pattern Recognition (ICPR). 2012; pages 1542–1545.
Anantharajah K, Ge ZY, McCool C, Denman S, Fookes C, Corke P, Tjondronegoro D, Sridharan S. IEEE winter conference on applications of computer vision. In: IEEE Winter Conference on Applications of Computer Vision. 2014; pages 309–316.
Chang CC, Jen C. Libsvm: a library for support vector machines. ACM Trans Intell Syst Technol. 2011;2(3):1–27.
Vedaldi A, Fulkerson B. Vlfeat: an open and portable library of computer vision algorithms. In: Proceedings of the 18th ACM International Conference on Multimedia. 2010; pages 1469–1472.
Tamou AB, Benzinou A, Nasreddine K, Ballihi L (2018) Underwater Live Fish Recognition by Deep Learning. In: Mansouri A, El Moataz A, Nouboud F, Mammass D (eds) Image and Signal Processing. ICISP 2018. Lecture Notes in Computer Science, vol 10884. Springer, Cham. https://doi.org/10.1007/978-3-319-94211-7_30
Zeiler M, Fergus D. Visualizing and understanding convolutional networks. Comput Vis - ECCV. 2014;2014:818–33.
Szegedy C, Liu W, Jia Y, Sermanet P, Reed SE, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A. Going deeper with convolutions. Comput Res Reposit. 2014;abs/1409.4842:1–12.
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Mathur, M., Goel, N. FishResNet: Automatic Fish Classification Approach in Underwater Scenario. SN COMPUT. SCI. 2, 273 (2021). https://doi.org/10.1007/s42979-021-00614-8
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DOI: https://doi.org/10.1007/s42979-021-00614-8