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http://dx.doi.org/10.6109/jkiice.2019.23.1.39

Application of CNN for Fish Species Classification  

Park, Jin-Hyun (Dept. of Mechatronics Engineering, Kyeognam National Univ. of Science and Technology)
Hwang, Kwang-Bok (Dept. of Mechatronics Engineering, Kyeognam National Univ. of Science and Technology)
Park, Hee-Mun (British American Tobacco Korea)
Choi, Young-Kiu (Department of Electrical Engineering, Pusan National University)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.23, no.1, 2019 , pp. 39-46 More about this Journal
Abstract
In this study, before system development for the elimination of foreign fish species, we propose an algorithm to classify fish species by training fish images with CNN. The raw data for CNN learning were directly captured images for each species, Dataset 1 increases the number of images to improve the classification of fish species and Dataset 2 realizes images close to natural environment are constructed and used as training and test data. The classification performance of four CNNs are over 99.97% for dataset 1 and 99.5% for dataset 2, in particular, we confirm that the learned CNN using Data Set 2 has satisfactory performance for fish images similar to the natural environment. And among four CNNs, AlexNet achieves satisfactory performance, and this has also the shortest execution time and training time, we confirm that it is the most suitable structure to develop the system for the elimination of foreign fish species.
Keywords
CNN(Convolutional Neural Network); Fish Image; Fish Species; AlexNet; Classification;
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1 S. I. Hassan, L. Dang, S. H. Im, K. B. Min, J. Y. Nam, and H. J. Moon, "Damage Detection and Classification System for Sewer Inspection using Convolutional Neural Networks based on Deep Learning," Journal of the Korea Institute of Information and Communication Engineering, vol. 22, no. 3, pp. 451-457, Mar. 2018.   DOI
2 J. H. Kim, D. S. Choi, H. S. Lee, and J. W. Lee, "Target Classification of Active Sonar Returns based on Convolutional Neural Network," Journal of the Korea Institute of Information and Communication Engineering, vol. 21 no. 10, pp. 1909-1916, Oct. 2017.   DOI
3 G. Chen, P. Sun, and Y. Shang, "Automatic Fish Classification System Using Deep Learning," Tools with Artificial Intelligence(ICTAI), 2017 IEEE 29th International Conference on. IEEE, pp. 24-29, 2017.
4 V. A. Sindagi, and V. M. Patel, "A Survey of Recent Advances in CNN-based Single Image Crowd Counting and Density Estimation," Pattern Recognition Letters, vol. 107, no. 1, pp. 3-16, May 2018.   DOI
5 M. Sarigul, and M. Avci, "Comparison of Different Deep Structures for Fish Classification," International Journal of Computer Theory and Engineering, vol. 9, no. 5, Oct. 2017.
6 H. Qin, X. Li, J. Liang, Y. Peng, and C. Zhang, "DeepFish: Accurate under water live fish recognition with a deep architecture," Neurocomputing, vol. 187 no. 26, pp. 49-58, Apr. 2016.   DOI
7 A. Salman, A. Jalal, F. S., A. Mian, M. Shortis, J. Seager, and E. Harvey, "Fish species classification in unconstrained underwater environments based on deep learning," LIMNOLOGY and OCEANOGRAPHY: METHODS, Association for the Sciences of Limnology and Oceanography, vol. 14, no. 9, pp. 570-585, Sep. 2016.   DOI
8 Stanford Vision Lab, Stanford University, Princeton University, Large Scale Visual Recognition Challenge, [Internet]. Available: www.image-net.org.
9 A. Krizhevsky, I. Sutskever, and G. E. Hinton, "Imagenet classification with deep convolutional neural networks," Advances in neural information processing systems 25(NIPS2012), pp. 1097-1105, 2012.
10 I. K Choi, H. E. Ahn, and J. S. Yoo, "Facial Expression Classification Using Deep Convolutional Neural Network," Journal of Electrical Engineering & Technology, vol. 13, no. 1, pp. 485-492, Jan. 2018.   DOI
11 K. Simonyan and A. Zisserman. (2015, May). Very deep convolutional networks for large-scale image recognition. 3rd International Conference on Learning Representations. [Internet]. Available: http://arxiv.org/abs/1409.1556.
12 MathWorks, Pretrained VGG-16 convolutional neural network, [Internet]. Available: https://kr.mathworks.com/help/deeplearning/ref/vgg16.html.
13 MathWorks, Pretrained VGG-19 convolutional neural network, [Internet]. Available: https://kr.mathworks.com/help/deeplearning/ref/vgg19.html.
14 MathWorks, Pretrained GoogLet convolutional neural network, [Internet]. Available: https://kr.mathworks.com/help/deeplearning/ref/googlenet.html.
15 G. E. Hinton, S. Osindero, and Y. Teh, "A fast learning algorithm for deep belief nets," Neural Computation, vol. 18, no. 7, pp. 1527-1554, Jul. 2006.   DOI
16 Y. Bengio, "Deep Learning of Representations for Unsupervised and Transfer Learning," Proceedings of Machine Learning Research, Volume 27: Proceedings of ICML Workshop on Unsupervised and Transfer Learning, Washington:WA, pp. 17-37, Jul. 2012.
17 J. Ba, and D. P. Kingma. (2015, May). Adam: A Method for Stochastic Optimization. 3rd International Conference on Learning Representations. [Internet]. Available: http://arxiv.org/abs/1412.6980.
18 Korea Institute for International Economic Policy. 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=globalecono&nttId=185515.
19 Y. Le Cun, L. Bottou, Y. Bengio, and P. Haffner, "Gradient-based learning applied to document recognition," Proceedings of the IEEE, vol. 86, no. 11, pp. 2278-2324, 1998.   DOI
20 Y. Bengio, "Learning deep architectures for AI," Foundations and Trends in Machine Learning, vol. 2, no. 1, pp. 1-127, Feb. 2009.   DOI