Fig. 1 CNN system[2-5]
Fig. 2 data augmentation
Fig. 3 Images in the water and composite fish images(a) Images of green algae and muddy water(b) Composite fish images
Fig. 4 Training Set and Training Progress (a) Training Set 1 of AlexNet (b) Training Progress of AlexNet
Fig. 5 Classification Result using Dataset 2 of AlexNet
Fig. 6 Correct Predicted Results
Fig. 7 Non Correct Predicted Results
Table. 1 The Dataset 1, Dataset 2
Table. 2 The Classification Performance of CNNs using Test Data in Dataset 1
Table. 3 The Classification Performance of CNNs learned by Dataset 1 using Test Data in Dataset 2
Table. 4 The Classification Performance of CNNs using Test Data in Dataset 2
참고문헌
- 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.
- 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. https://doi.org/10.1109/5.726791
- Y. Bengio, "Learning deep architectures for AI," Foundations and Trends in Machine Learning, vol. 2, no. 1, pp. 1-127, Feb. 2009. https://doi.org/10.1561/2200000006
- 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. https://doi.org/10.1162/neco.2006.18.7.1527
- 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. https://doi.org/10.6109/JKIICE.2018.22.3.451
- 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. https://doi.org/10.6109/JKIICE.2017.21.10.1909
- 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.
- 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. https://doi.org/10.1016/j.patrec.2017.07.007
- 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.
- 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. https://doi.org/10.1016/j.neucom.2015.10.122
- 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. https://doi.org/10.1002/lom3.10113
- Stanford Vision Lab, Stanford University, Princeton University, Large Scale Visual Recognition Challenge, [Internet]. Available: www.image-net.org.
- 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. https://doi.org/10.5370/JEET.2018.13.1.485
- 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.
- 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.
- MathWorks, Pretrained VGG-16 convolutional neural network, [Internet]. Available: https://kr.mathworks.com/help/deeplearning/ref/vgg16.html.
- MathWorks, Pretrained VGG-19 convolutional neural network, [Internet]. Available: https://kr.mathworks.com/help/deeplearning/ref/vgg19.html.
- MathWorks, Pretrained GoogLet convolutional neural network, [Internet]. Available: https://kr.mathworks.com/help/deeplearning/ref/googlenet.html.
- 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.
- 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.
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