CNN 기반의 물고기 탐지 알고리즘 구현

Implementation of Fish Detection Based on Convolutional Neural Networks

  • 이용환 (원광대학교 디지털콘텐츠공학과) ;
  • 김흥준 (경남과학기술대학교 컴퓨터공학과)
  • Lee, Yong-Hwan (Dept. of Digital Contents, Wonkwang University) ;
  • Kim, Heung-Jun (Dept. of Computer Science and Engineering, Gyeongnam National University of Science and Technology)
  • 투고 : 2020.09.21
  • 심사 : 2020.09.23
  • 발행 : 2020.09.30

초록

Autonomous underwater vehicle makes attracts to many researchers. This paper proposes a convolutional neural network (CNN) based fish detection method. Since there are not enough data sets in the process of training, overfitting problem can be occurred in deep learning. To solve the problem, we apply the dropout algorithm to simplify the model. Experimental result showed that the implemented method is promising, and the effectiveness of identification by dropout approach is highly enhanced.

키워드

참고문헌

  1. R. B. Wynn, V. A. I. Huvenne, T. P. Le Bas et al., "Autonomous underwater vehicles (AUVs): their past, present and future contributions to the advancement of marine geoscience," Marine Geology, vol. 352, pp. 451-468, 2014. https://doi.org/10.1016/j.margeo.2014.03.012
  2. M. Dinc and C. Hajiyev, "Integration of navigation systems for autonomous underwater vehicles," Journal of Marine Engineering & Technology, vol. 14, no. 1, pp. 32-43, 2015. https://doi.org/10.1080/20464177.2015.1022382
  3. C. Winchester, J. Govar, J. Banner, T. Squires, P. Smith, "A survey of available underwater electric propulsion technologies and implications for platform system safety", Workshop on Autonomous Underwater Vehicles, 2002.
  4. Bo Zhang, "Computer Vision vs. Human Vision", International Conference on Cognitive Informatics, 2010.
  5. C.-F. Chien, Y.-J. Chen, Y.-T. Han et al., "AI and big data analytics for wafer fab energy saving and chiller optimization to empower intelligent manufacturing," Proceedings of e-Manufacturing & Design Collaboration Symposium, pp. 1-4, 2018.
  6. S. Biswas, Y. Wang, S. Cui, "Surgically altered face detection using log-gabor wavelet", International Conference on Wavelet Active Media Technology and Information Processing, pp. 154-157, 2015.
  7. Eman Abdel-Maksoud, Mohammed Elmogy, and Rashid Al-Awadi, "Brain tumor segmentation based on a hybrid clustering technique," Egyptian Informatics Journal, vol. 16, no. 1, pp. 71-81, 2015. https://doi.org/10.1016/j.eij.2015.01.003
  8. Y. Wang, Y. Lan, Y. Zheng, K. Lee, S. Cui, and J. Lian, "A UGV-based laser scanner system for measuring tree geometric characteristics," International Symposium on Photoelectronic Detection and Imaging, vol. 8905, 2013.
  9. B. Marr, "Key milestones of Waymo - Google's selfdriving cars," https://www.forbes.com/sites/bernardmarr/2018/09/21/key-milestones-of-waymo-googles-self-driving-cars/#3831b2965369
  10. Y. LeCun, 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
  11. Manish I. Patel, Sirali Suthar, Jil Thakar, "Survey on Image Compression using Machine Learning and Deep Learning", International Conference on Intelligent Computing and Control Systems, 2019.
  12. Jonathan Rogers, Dylan Simmons, Milesh Shah, Connor Rowland, Yi Shang, "Deep Learning at Your Fingertips", Consumer Communication and Networking Conference, 2019.
  13. A. Krizhevsky, I. Sutskever, and G. E. Hinton, "ImageNet classification with deep convolutional neural networks," Advances in Neural Information Processing Systems, vol. 25, pp. 1106-1114, 2012.
  14. R. Girshick, "Fast R-CNN," in 2015 IEEE International Conference on Computer Vision, pp. 1440-1448, 2015.
  15. Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, Philip S. Yu, "A Comprehensive Survey on Graph Neural Networks", IEEE Transactions on Neural Networks and Learning Systems, pp.1-21, 2020.
  16. S. Hassairi, R. Ejbali, and M. Zaied, "A deep convolutional neural wavelet network to supervised Arabic letter image classification," International Conference on Intelligent Systems Design and Applications, pp. 207-212, 2015.
  17. D. Zhang, G. Kopanas, C. Desai, S. Chai, and M. Piacentino, "Unsupervised underwater fish detection fusing flow and objectiveness", Winter Applications of Computer Vision Workshops, pp. 1-7, 2016.
  18. Deep learning and machine learning, https://ireneli.eu/2016/02/03/deep-learning-05-talk-about-convolutionalneural-network.
  19. J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, "You only look once: unified, real-time object detection", Conference on Computer Vision and Pattern Recognition, pp. 779-788, 2016.
  20. Website: ImageNet, http://image-net.org/about-overview.
  21. J. Gaya, L. T. Gonçalves, A. Duarte, B. Zanchetta, P. Drews, S. Botelho, "Vision-based obstacle avoidance using deep learning," Latin American Robotics Symposium and Brazilian Robotics Symposium, pp. 7-12, 2016.
  22. Ledan Qian, Libing Hu, Li Zhao, Tao Wang, Runhua Jiang, "Sequence-Dropbox Block for Reducing Overfitting Problem in Image Classification", IEEE Access, vol.8, 2020.
  23. N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, R. Salakhutdinov, "Dropout: a simple way to prevent neural networks from overfitting", Journal of Machine Learning Research, vol. 15, pp. 1929-1958, 2014.
  24. Jaya S. Kulchandani, Kruti J. Dangarwala, "Moving Object Detection: Review of Resent Research Trends", International Conference on Pervasive Computing, 2015.
  25. Aayushi Gautam, Sukhwinder Singh, "Trends in Video Object Tracking in Surveillance: A Survey", International Conference on IoT in Social Mobile Analytics and Cloud, 2019.
  26. Sonali S. Mengane, Amar A. Dum, "Improved Object Tracking Techniques using Hybrid Approach", International Conference on Trends in Electronics and Informatics, 2019.
  27. Hyng-il Kim, Woontack Woo, "Smartwatch-assisted Robust 6-DOF Hand Tracker for Object Manipulation in HMD-based Augmented Reality", IEEE Symposium on 3D User Interfaces, 2016.