Fish Classification Using Deep Learning on Small Scale and Low-Quality Images


  • M. Sudhakara Research Scholar, SCOPE, VITCC, Chennai, India
  • M. Janaki Meena Professor, SCOPE, VITCC, Chennai, India
  • K. Reddy Madhavi Professor, C.S.E., Sree Vidyanikethan Engineering College, Tirupati
  • P. Anjaiah Assistant Professor, Department of C.S.E, Institute of Aeronautical Engineering, Hyderabad, India
  • LNC Prakash K Associate Professor, C.S.E. Department, CVR Engineering College, Mangalpalli, Hyderabad


Fish classification, CycleGAN, DCGAN, SmallerVGG, SmallerRESNET


Fine-grained visual classification is one of the essential data science tasks with enormous datasets. The studies show that species composition and ample distribution of fishes notably impact the fishery industry, aquaculture, and marine ecosystem. Incredible work and analysis are required to state fish characteristics by classification. Lately, deep learning has helped to gain exceptional development in this area. Be that as it may, fine-grained fish classification is more complex than primary image classification, particularly with medium quality (i.e., underwater images) and small-scale (i.e., limited data). But traditional convolutional neural networks (CNNs) and other popular models like V.G.G., RESNET, DenseNet, etc., require high-quality and high-scale datasets. This paper presents another way to enhance the CNN models that best fit this fine-grained fish classification problem. Real-world underwater images have several issues, including noise, dominant colours, light attenuation, etc. Further, it isn't easy to get a large set of images of each category of species under the sea, and hence an imbalanced dataset is generated. These two problems are addressed in this paper. Then the quality of the raw images was improved by an Underwater Image Enhanced Generative Adversarial Network (UIEGAN), that CycleGAN trains over 6128 images of the ImageNet dataset. Conventional data augmentation helps increase the dataset size of the dataset by random transformations of the images (i.e., flipping, rotation), but it cannot handle the imbalanced class problem. We generated synthetic images of every class utilizing DCGAN to create a balanced dataset. Further, we used the SmallerVGG and SmallerRESNET models that best fit the Croatian dataset. Moreover, we compared our strategy with eight popular pre-trained transfer learning models trained on the ImageNet dataset. The exploratory outcomes show that the proposed techniques beat well-known CNNs, with high accuracy, demonstrating their possible applications in the real-time underwater fish image classification.


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Zhuang, Peixian, Chongyi Li, and Jiamin Wu. "Bayesian retinex underwater image enhancement." Engineering Applications of Artificial Intelligence 101 (2021): 104171.

Shortis, M., Abdo, E.H.D., 2016. A review of underwater stereo-image measurement for marine biology and ecology applications. In: Oceanography and Marine Biology. C.R.C. Press, pp. 269–304.

Liu, Shasha, et al. "Embedded online fish detection and tracking system via YOLOv3 and parallel correlation filter." OCEANS 2018 MTS/IEEE Charleston. IEEE, 2018.

Xu, Wenwei, and Shari Matzner. "Underwater fish detection using deep learning for water power applications." 2018 International Conference on Computational Science and Computational Intelligence (CSCI). IEEE, 2018.

Gupta, D. J. . (2022). A Study on Various Cloud Computing Technologies, Implementation Process, Categories and Application Use in Organisation. International Journal on Future Revolution in Computer Science &Amp; Communication Engineering, 8(1), 09–12.

Yadav, P. ., S. . Kumar, and D. K. J. . Saini. “A Novel Method of Butterfly Optimization Algorithm for Load Balancing in Cloud Computing”. International Journal on Recent and Innovation Trends in Computing and Communication, vol. 10, no. 8, Aug. 2022, pp. 110-5, doi:10.17762/ijritcc.v10i8.5683.

Pedersen, Malte, et al. “3D-ZEF: A 3D zebrafish tracking benchmark dataset.” Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2020.

Knausgård, Kristian Muri, et al. "Temperate fish detection and classification: A deep learning-based approach." Applied Intelligence (2021): 1-14.

L. Meng, T. Hirayama, and S. Oyanagi, "Underwater-drone with panoramic camera for automatic fish recognition based on deep learning," IEEE Access, vol. 6, pp. 17880–17886, 2018.

Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, "Gradient-based learning applied to document recognition," Proc. IEEE, Vol. 86, no. 11, pp. 2278–2324, Nov. 1998.

A. Krizhevsky, I. Sutskever, and G. E. Hinton, "ImageNet classification with deep convolutional neural networks," in Proc. Adv. Neural Inf. Process. Syst., 2012, pp. 1097–1105.

C. Szegedy et al., "Going deeper with convolutions," in Proc. IEEE Int. Conf. Comput. Vis. Pattern Recognit., Jun. 2015, pp. 1–9.

H. Qin, X. Li, J. Liang, Y. Peng, and C. Zhang, "DeepFish: Accurate underwater live fish recognition with a deep architecture," Neurocomputing, vol. 187, pp. 49–58, Apr. 2016.

Qiu, Chenchen, et al. "Improving transfer learning and squeeze-and-excitation networks for small-scale fine-grained fish image classification." IEEE Access 6 (2018): 78503-78512.

Zhao, Zhenxi, et al. "Composited FishNet: Fish Detection and Species Recognition from Low-quality Underwater Videos." IEEE Transactions on Image Processing (2021).

X.-S. Wei, C.-W. Xie, J. Wu, and C. Shen, "Mask-CNN: Localizing parts and selecting descriptors for fine-grained bird species categorization," Pattern Recognit., vol. 76, pp. 704–714, Apr. 2018.

J. Jaeger, M. Simon, J. Denzler, V. Wolff, K. Fricke-Neuderth, and C. Kruschel, "Croatian fish dataset: Fine-grained classification of fish species in their natural habitat," in Proc. Mach. Vis. Animals Behav., 2015, pp. 1–7.

C. Ledig et al. (Sep. 2016). "Photo-realistic single image super-resolution using a generative adversarial network." [Online]. Available: https://arxiv. org/abs/1609.04802.

Cho, Se Woon, et al. "Semantic segmentation with low light images by modified CycleGAN-based image enhancement." IEEE Access 8 (2020): 93561-93585.

Goodfellow, Ian J., Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. "Generative adversarial networks." arXiv preprint arXiv:1406.2661 (2014).

Denton, Emily, Chintala, Soumith, Szlam, Arthur, and Fergus, Rob. Deep generative image models using a laplacian pyramid of adversarial networks. arXiv preprint arXiv:1506.05751, 2015.

Radford, Alec, Luke Metz, and Soumith Chintala. "Unsupervised representation learning with deep convolutional generative adversarial networks." arXiv preprint arXiv:1511.06434 (2015).

Yu, Yang, et al. "Unsupervised representation learning with deep convolutional neural network for remote sensing images." International Conference on Image and Graphics. Springer, Cham, 2017.

Szegedy, Christian, et al. "Inception-v4, inception-resnet and the impact of residual connections on learning." Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 31. No. 1. 2017.

He, Kaiming, et al. "Deep residual learning for image recognition." Proceedings of the IEEE conference on computer vision and pattern recognition. 2016.

M. J. Traum, J. Fiorentine. (2021). Rapid Evaluation On-Line Assessment of Student Learning Gains for Just-In-Time Course Modification. Journal of Online Engineering Education, 12(1), 06–13. Retrieved from

Simonyan, Karen, and Andrew Zisserman. "Very deep convolutional networks for large-scale image recognition." arXiv preprint arXiv:1409.1556 (2014).

Szegedy, Christian, et al. "Rethinking the inception architecture for computer vision." Proceedings of the IEEE conference on computer vision and pattern recognition. 2016.

Chollet, François. "Xception: Deep learning with depthwise separable convolutions." Proceedings of the IEEE conference on computer vision and pattern recognition. 2017.

Kumar, S., Gornale, S. S., Siddalingappa, R., & Mane, A. (2022). Gender Classification Based on Online Signature Features using Machine Learning Techniques. International Journal of Intelligent Systems and Applications in Engineering, 10(2), 260–268. Retrieved from

Huang, Gao, et al. "Densely connected convolutional networks." Proceedings of the IEEE conference on computer vision and pattern recognition. 2017.

Howard, Andrew G., et al. "Mobilenets: Efficient convolutional neural networks for mobile vision applications." arXiv preprint arXiv:1704.04861 (2017).

Saraireh, J., & Joudeh, H. (2022). An Efficient Authentication Scheme for Internet of Things. International Journal of Communication Networks and Information Security (IJCNIS), 13(3).

Zoph, Barret, et al. "Learning transferable architectures for scalable image recognition." Proceedings of the IEEE conference on computer vision and pattern recognition. 2018.

M. Sudhakara and M. Janaki Meena, "Multi-scale fusion for underwater image enhancement using multi-layer perceptron," IAES International Journal of Artificial Intelligence (IJ-AI), Vol. 10, No. 2, June 2021, pp. 389~397. DOI: 10.11591/ijai.v10.i2.pp389-397.

Krizhevsky, Alex, Ilya Sutskever, and Geoffrey E. Hinton. "Imagenet classification with deep convolutional neural networks." Advances in neural information processing systems 25 (2012): 1097-1105.

Szegedy, Christian, et al. "Inception-v4, inception-resnet and the impact of residual connections on learning." Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 31. No. 1. 2017.

Singh, S. ., Wable, S. ., & Kharose, P. . (2022). A Review Of E-Voting System Based on Blockchain Technology. International Journal of New Practices in Management and Engineering, 10(04), 09–13.

K. Anantharajah et al., "Local inter-session variability modelling for object classification," in Proc. IEEE Winter Conf. Appl. Comput. Vis., Mar. 2014, pp. 309–316

T.-Y. Lin, A. RoyChowdhury, and S. Maji, "Bilinear CNN models for fine-grained visual recognition," in Proc. IEEE Int. Conf. Comput. Vis., Dec. 2015, pp. 1449–1457.

J. Hu, E. Wu, L. Shen, and G. Sun. (Sep. 2017). "Squeeze-and-excitation networks." [Online]. Available:

The flow of our proposed methodology




How to Cite

M. . Sudhakara, M. J. . Meena, K. R. . Madhavi, P. . Anjaiah, and L. P. . K, “Fish Classification Using Deep Learning on Small Scale and Low-Quality Images”, Int J Intell Syst Appl Eng, vol. 10, no. 1s, pp. 279 –, Oct. 2022.