지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- Korean Institute of Information Scientists and Engineers Journal of Computing Science and Engineering Journal of Computing Science and Engineering Vol.13 No.3
- 발행연도
- 2019.9
- 수록면
- 124 - 130 (7page)
- DOI
- 10.5626/JCSE.2019.13.3.124
이용수
초록· 키워드
오류제보하기
Conservation and protection of fish species is very important in aquaculture and marine biology. A few studies have introduced the concept of fish recognition; however, it resulted in poor rates of error recognition and conservation of a small number of species. This study presents a fish recognition method based on deep convolutional neural networks such as VGG16, which was pre-trained on ImageNet via transfer learning method. The fish dataset in this study consists of 50 species, each covered by 15 images including 10 images for training purpose and 5 images for testing. In this study, we trained our model on four different types of dataset: RGB color space image, canny filter image, blending image, and blending image mixed with RGB image. The results showed that blending image mixed with RGB image trained model exhibited the best genuine acceptance rate (GAR) value of 96.4%, following by the RGB color space image trained model with a GAR value of 92.4%, the canny filter image trained model with a GAR value of 80.4%, and the blending image trained model showed the least GAR value of 75.6%.
목차
Abstract
I. INTRODUCTION
II. RELATED WORK
III. PROPOSED METHOD
IV. EXPERIMENTAL RESULTS AND ANALYSES
V. CONCLUSION
REFERENCES
참고문헌 (20)
참고문헌 신청
G. Helfman, B. B. Collette, D. E. Facey, and B. W. Bowen, The Diversity of Fishes: Biology, Evolution, and Ecology. Chichester: Wiley-Blackwell, 2009.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, 2016, pp. 770-778.
V. Allken, N. O. Handegard, S. Rosen, T. Schreyeck, T. Mahiout, and K. Malde, “Fish species identification using a convolutional neural network trained on synthetic data,” ICES Journal of Marine Science, vol. 76, no. 1, pp. 342-349, 2018.
D. Rathi, S. Jain, and S. Indu, “Underwater fish species classification using convolutional neural network and deep learning,” in Proceedings of 2017 9th International Conference on Advances in Pattern Recognition (ICAPR), Bangalore, India, 2017, pp. 1-6.
M. M. Ghazi, B. Yanikoglu, and E. Aptoula, “Plant identification using deep neural networks via optimization of transfer learning parameters,” Neurocomputing, vol. 235, pp. 228-235, 2017.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, 2016, pp. 770-778.
V. Allken, N. O. Handegard, S. Rosen, T. Schreyeck, T. Mahiout, and K. Malde, “Fish species identification using a convolutional neural network trained on synthetic data,” ICES Journal of Marine Science, vol. 76, no. 1, pp. 342-349, 2018.
D. Rathi, S. Jain, and S. Indu, “Underwater fish species classification using convolutional neural network and deep learning,” in Proceedings of 2017 9th International Conference on Advances in Pattern Recognition (ICAPR), Bangalore, India, 2017, pp. 1-6.
M. M. Ghazi, B. Yanikoglu, and E. Aptoula, “Plant identification using deep neural networks via optimization of transfer learning parameters,” Neurocomputing, vol. 235, pp. 228-235, 2017.
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