대한조선학회논문집 (Journal of the Society of Naval Architects of Korea)

  • 제57권3호
  • /
  • Pages.140-151
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  • 2020
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  • 1225-1143(pISSN)
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  • 2287-7355(eISSN)
대한조선학회 (The Society of Naval Architects of Korea)
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다시점 영상 집합을 활용한 선체 블록 분류를 위한 CNN 모델 성능 비교 연구

Comparison Study of the Performance of CNN Models with Multi-view Image Set on the Classification of Ship Hull Blocks

  • 전해명 (군산대학교 조선해양공학과) ;
  • 노재규 (군산대학교 조선해양공학과)
  • Chon, Haemyung (Department of Naval Architecture and Ocean Engineering, Kunsan National University) ;
  • Noh, Jackyou (Department of Naval Architecture and Ocean Engineering, Kunsan National University)
  • 투고 : 2020.03.02
  • 심사 : 2020.03.24
  • 발행 : 2020.06.20
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초록

It is important to identify the location of ship hull blocks with exact block identification number when scheduling the shipbuilding process. The wrong information on the location and identification number of some hull block can cause low productivity by spending time to find where the exact hull block is. In order to solve this problem, it is necessary to equip the system to track the location of the blocks and to identify the identification numbers of the blocks automatically. There were a lot of researches of location tracking system for the hull blocks on the stockyard. However there has been no research to identify the hull blocks on the stockyard. This study compares the performance of 5 Convolutional Neural Network (CNN) models with multi-view image set on the classification of the hull blocks to identify the blocks on the stockyard. The CNN models are open algorithms of ImageNet Large-Scale Visual Recognition Competition (ILSVRC). Four scaled hull block models are used to acquire the images of ship hull blocks. Learning and transfer learning of the CNN models with original training data and augmented data of the original training data were done. 20 tests and predictions in consideration of five CNN models and four cases of training conditions are performed. In order to compare the classification performance of the CNN models, accuracy and average F1-Score from confusion matrix are adopted as the performance measures. As a result of the comparison, Resnet-152v2 model shows the highest accuracy and average F1-Score with full block prediction image set and with cropped block prediction image set.

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참고문헌

  1. Byeon, Y.H. & Kwak, K.C., 2018. A transfer learning and performance comparison of deep learning models for pedestrian classification under automobile driving environment. The Journal of Korean Institute of Information Technology, 16(10), pp.83-92. https://doi.org/10.14801/jkiit.2018.16.10.83
  2. Cho, D.Y., Song, H.C. & Cha, J.H., 2011. Block and logistics simulation. Bulletin of the Society of Naval Architects of Korea, 48(4), pp.24-29.
  3. Donahue, J. et al., 2014. Decaf: A deep convolutional activation feature for generic visual recognition. In International conference on machine learning, Beijing, China, pp.647-655.
  4. Francois, C., 2018. Deep learning with Python, Gilbut Publichin Co,. Ltd.
  5. He, K., Zhang, X., Ren, S., & Sun, J., 2016. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, Las Vegas, United States of America, pp.770-778.
  6. Huang, G., Liu, Z., Van Der Maaten, L. & Weinberger, K.Q., 2017. Densely connected convolutional networks. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp.4700-4708.
  7. Kang, J.H., 2014. A study on mobile block logistics system for shipyard. Master Thesis, Mokpo National University.
  8. Kim, B.C., 2018. Shape recognition of plant equipment from 3-D scanned point cloud data using a convolutional neural network. Transactions of the Korean Society of Mechanical Engineers, A 42(9), pp.863-869. https://doi.org/10.3795/KSME-A.2018.42.9.863
  9. Kim, J.O. et al., 2009. Development of real time location measuring and logistics system for assembled block in shipbuilding. Korean Institute of Industrial Engineers, pp.834-839.
  10. Kim, M.S., Cha, J.H. & Cho, D.Y., 2013. Determination of arrangement and take-out path in ship block stockyard considering available space and obstructive block. Society for Computational Design and Engineering, pp.433-438.
  11. Krizhevsky, A., Sutskever, I. & Hinton, G. E., 2012. Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems, pp.1097-1105.
  12. LeCun, Y., Bottou, L., Bengio, Y. & Haffner, P., 1998. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), pp.2278-2324. https://doi.org/10.1109/5.726791
  13. Lee, Y.H., Lee, K.C., Lee, K.J. & Son, Y.D., 2008. Study on the positioning system for logistics of ship-block. Special Issue of the Society of Naval Architects of Korea, pp.68-75.
  14. Mun, S.H., 2019. Real time block locating system for shipbuilding through GNSS and IMU fusion. Ph.D. Thesis, Pusan National University.
  15. Nam, B.W., Lee, K.H., Lee, J.J. & Mun. S.H., 2017. A study on selection of block stockyard applying decision tree learning algorithm. Journal of the Society of Naval Architects of Korea, 54(5), pp.421-429. https://doi.org/10.3744/SNAK.2017.54.5.421
  16. Pan, S. J., & Yang, Q., 2009. A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), pp.1345-1359. https://doi.org/10.1109/TKDE.2009.191
  17. Park, S.W. & Kim, D.Y., 2018. Comparison of image classification performance in convolutional neural network according to transfer learning. Journal of Korea Multimedia Society, 21(12), pp.1387-1395. https://doi.org/10.9717/KMMS.2018.21.12.1387
  18. Saito, G., 2017. Deep learning from scratch, Hanbit Media, lnc.
  19. Shin, J.G. & Lee, J.H., 2006. Prototype of block tracing system for pre-erection area using PDA and GPS. Journal of the Society of Naval Architects of Korea, 43(1), pp.87-95. https://doi.org/10.3744/SNAK.2006.43.1.087
  20. Simonyan, K. & Zisserman, A., 2014. Very deep convolutional networks for large-scale image recognition. Published as a conference paper at ICLR 2015, San Diego, United States of America, September 2014.
  21. Srivastava, N. et al., 2014. Dropout: a simple way to prevent neural networks from overfitting. The journal of machine learning research, 15(1), pp.1929-1958.
  22. Standford University, 2018. ILSVRC, URL : http://image-net.org/challenges/LSVRC/ [Accessed 30 November 2019].
  23. Szegedy, C. et al., 2014. Going deeper with convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp.1-9.
  24. Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J. & Wojna, Z., 2016. Rethinking the inception architecture for computer vision. In Proceedings of the IEEE conference on computer vision and pattern recognition, Las Vegas, United States of America, pp.2818-2826.
  25. Yosinski, J., Clune, J., Bengio, Y. & Lipson, H., 2014. How transferable are features in deep neural networks?. In Advances in neural information processing systems, pp.3320-3328.
  26. Zeiler, M.D. & Fergus, R., 2014. Visualizing and understanding convolutional networks. In European conference on computer vision, Zurich, Swiss, pp.818-833.
  27. Zhou, B., Khosla, A., Lapedriza, A., Oliva, A. & Torralba, A., 2016. Learning deep features for discriminative localization. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 2921-2929.
  28. Zhou, B. et al., 2014. Object detectors emerge in deep scene cnns. In International Conference on Learning Representations, San Diego, United State of America.
  29. Zoph, B., Vasudevan, V., Shlens, J. & Le, Q.V., 2018. Learning transferable architectures for scalable image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2018, Salt Lake Coty, United States of America, pp.8697-8710.