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Efficient Data Acquisition and CNN Design for Fish Species Classification in Inland Waters

  • Park, Jin-Hyun (Department of Mechatronics Engineering, Gyeongnam National Univ. of Science and Technology) ;
  • Choi, Young-Kiu (Department of Electrical Engineering, Pusan National University)
  • Received : 2020.03.09
  • Accepted : 2020.06.22
  • Published : 2020.06.30

Abstract

We propose appropriate criteria for obtaining fish species data and number of learning data, as well as for selecting the most appropriate convolutional neural network (CNN) to efficiently classify exotic invasive fish species for their extermination. The acquisition of large amounts of fish species data for CNN learning is subject to several constraints. To solve these problems, we acquired a large number of fish images for various fish species in a laboratory environment, rather than a natural environment. We then converted the obtained fish images into fish images acquired in different natural environments through simple image synthesis to obtain the image data of the fish species. We used the images of largemouth bass and bluegill captured at a pond as test data to confirm the effectiveness of the proposed method. In addition, to classify the exotic invasive fish species accurately, we evaluated the trained CNNs in terms of classification performance, processing time, and the number of data; consequently, we proposed a method to select the most effective CNN.

Keywords

References

  1. Convention on Biological Diversity, UN environment programme. [Internet], Available: https://www.cbd.int/abs/default.shtml.
  2. Korea Institute for International Economic Policy. 2010, 10th Session of the Conference of the Parties to the Convention on Biological Diversity: Nagoya Protocol, [Internet], Available: http://www.kiep.go.kr/sub/view.do?bbsId=global_econo&nttId=185515.
  3. H. W. Qin, X. Li, J. Liang, Y. G. Peng, and C. S. Zhang, "DeepFish: Accurate underwater live fish recognition with a deep architecture," Neurocomputing, vol. 187, no. 26, pp. 49-58, 2016. DOI: 10.1016/j.neucom.2015.10.122
  4. D. J. Lee, R. B. Schoenberger, D. Shiozawa, X. Xu, and P. Zhan, "Contour matching for a fish recognition and migration-monitoring system," in: Two-and Three-Dimensional Vision Systems for Inspection, Control, and Metrology II, International Society for Optics and Photonics, vol. 5606, pp. 37-48, 2004. DOI: 10.1117/12.571789
  5. N. J. C. Strachan, P. Nesvadba, and A. R. Allen, "Fish species recognition by shape-analysis of images," Pattern Recognition, vol. 23, no. 5, pp. 539-544, 1990. https://doi.org/10.1016/0031-3203(90)90074-U
  6. D. J. White, C. Svellingen, and N. J. C. Strachan, "Automated measurement of species and length of fish by computer vision," Fisheries Research, vol. 80, no. 2-3, pp. 203-210, 2006. DOI: 10.1016/j.fishres.2006.04.009
  7. N. J. C. Strachan, "Recognition of fish species by colour and shape," Image Vision Computing, vol. 11, no. 1, pp. 2-10, 1993. DOI: 10.1016/0262-8856(93)90027-E.
  8. G. French, M. Fisher, M. Mackiewicz, and C. Needle, "Convolutional neural networks for counting fish in fisheries surveillance video," in Proceedings of the Machine Vision of Animals and their Behaviour (MVAB), BMVA Press, pp. 7.1-7.10, 2015. DOI: 10.5244/C.29.MVAB.7
  9. A. Salman, A. Jalal, F. Shafait, A. Mian, M. Shortis, J. Seager, and E. Harvey, "Fish species classification in unconstrained underwater environments based on deep learning," Limnology and Oceanography: Methods, vol. 14, no. 9, pp. 570-585, 2016. https://doi.org/10.1002/lom3.10113
  10. J. H. Park, K. B. Hwang, H. M. Park, and Y. K. Choi, "Application of CNN for Fish Species Classification," Journal of the Korea Institute of Information and Communication Engineering, vol. 23, no. 1, pp. 39-46, 2019. https://doi.org/10.6109/JKIICE.2019.23.1.39
  11. G. Lopez, L. Quesada, and L.A. Guerrero, "Alexa vs. Siri vs. Cortana vs. Google Assistant: a comparison of speech-based natural user interfaces," in International Conference on Applied Human Factors and Ergonomics, Springer, pp. 241-250, 2017.
  12. Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner, "Gradient-based learning applied to document recognition," in Proceedings of the IEEE, vol. 86, no. 11, pp. 2278-2324, 1998. https://doi.org/10.1109/5.726791
  13. G. E. Hinton, S. Osindero, and Y. W. Teh, "A fast learning algorithm for deep belief nets," Neural Computing, vol. 18, no. 7, pp. 1527-1554, 2006. https://doi.org/10.1162/neco.2006.18.7.1527
  14. Y. Bengio, "Learning deep architectures for AI," Foundations and trends in Machine Learning, vol. 2, no. 1, pp. 1-127, 2009. https://doi.org/10.1561/2200000006
  15. J. H. Park, K. B. Hwang, and Y. K. Choi, "Design of CNN with MLP Layer," Journal of Korean Society of Mechanical Technology, vol. 20, no. 6, pp. 776-782, 2018. https://doi.org/10.17958/ksmt.20.6.201812.776
  16. A. Krizhevsky, I. Sutskever, and G. E. Hinton, "ImageNet Classification with Deep Convolutional Neural Networks," in Advances in neural information processing systems 25, pp. 1097-1105, 2012.
  17. E. Rezende, G. Ruppert, T. Carvalho, A. Theophilo, F. Ramos, and P. de Geus, "Malicious software classification using VGG16 deep neural network's bottleneck features," in Information Technology-New Generations, Springer, pp. 51-59, 2018.
  18. L. Chen, H. Zhang, J. Xiao, L. Nie, J. Shao, W. Liu, and T.-S. Chua, "Sca-cnn: Spatial and channel-wise attention in convolutional networks for image captioning," in Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 5659-5667, 2017.
  19. C. Liu, Y. Cao, Y. Luo, G. Chen, V. Vokkarane, and Y. Ma, "Deepfood: Deep learning-based food image recognition for computer-aided dietary assessment," in International Conference on Smart Homes and Health Telematics, Springer, pp. 37-48, 2016.
  20. Stanford Vision Lab, Stanford University, Princeton University, Large Scale Visual Recognition Challenge, [Internet], Available: https:/www.image-net.org.
  21. The Courant Institute of Mathematical Sciences, New York University, THE MNIST DATABASE of handwritten digits, [Internet], Available: http://yann.lecun.com/exdb/mnist/
  22. CIFAR, [Internet], Available: https://www.cifar.ca/
  23. D. P. Kingma and J. Ba, Adam: A method for stochastic optimization, 2014, [online] Available: https://arxiv.org/abs/1412.6980.