IEMEK Journal of Embedded Systems and Applications (대한임베디드공학회논문지)

Institute of Embedded Engineering of Korea (대한임베디드공학회)
권호 표지
DOI QR코드

DOI QR Code

Vision Sensor and Deep Learning-based Around View Monitoring System for Ship Berthing

비전 센서 및 딥러닝 기반 선박 접안을 위한 어라운드뷰 모니터링 시스템

  • Received : 2020.02.29
  • Accepted : 2020.03.28
  • Published : 2020.04.30
Download PDF
⟨ Previous Next ⟩

Abstract

This paper proposes vision sensors and deep learning-based around view monitoring system for ship berthing. Ship berthing to the port requires precise relative position and relative speed information between the mooring facility and the ship. For ships of Handysize or higher, the vesselships must be docked with the help of pilots and tugboats. In the case of ships handling dangerous cargo, tug boats push the ship and dock it in the port, using the distance and velocity information receiving from the berthing aid system (BAS). However, the existing BAS is very expensive and there is a limit on the size of the vessel that can be measured. Also, there is a limitation that it is difficult to measure distance and speed when there are obstacles near the port. This paper proposes a relative distance and speed estimation system that can be used as a ship berthing assist system. The proposed system is verified by comparing the performance with the existing laser-based distance and speed measurement system through the field tests at the actual port.

Keywords

References

  1. Korea Coast Guard, Statistical Yearbook of Maritime Distress in 2018 (in Korean).
  2. Korean Statistical Information Service, Status of Marine Accidents by Ship Use (in Korean).
  3. AVISS Sensor Module, Seadronix, Available on : http://www.seadronix.com/products.
  4. H. Kim, J. Koo, D. Kim, B. Park, Y. Jo, H. Myung, D. Lee, "Vision-based Real-time Obstacle Segmentation Algorithm for Autonomous Surface Vehicle," Journal of IEEE Access, Vol. 7, pp. 179420-179428, 2019. https://doi.org/10.1109/ACCESS.2019.2959312
  5. A. G. Howard, M. Zhu, B. Chen, D. Kalenichenko, W. Wang, T. Weyand, M. Andreetto, H. Adam, "MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications," pp. 1-9, 2017.
  6. A. Pazke, A. Chaurasia, S. Kim, E. Culurciello, "ENet: A Deep Neural Network Architecture for Real-time Semantic Segmentation", pp. 1-10, 2017.
  7. L.-C. Chen, Y. Zhu, G. Papandreou, F. Schoff, H. Adam, "Encoder-decoder with Atrous Separable Convolution for Semantic Image Segmentation," Proceedings of European Conference on Computer Cision, pp. 833-851, 2018.
  8. H.-K. Kim, K.-Y. Yoo, J. H. Park, H.-Y. Jung, "Deep Learning Based Gray Image Generation from 3D LiDAR Reflection Intensity," IEMEK J. Embed. Sys. Appl., Vol. 14, No. 1, pp. 1-9, 2019 (in Korean). https://doi.org/10.14372/IEMEK.2019.14.1.1
  9. Y. Kim, H. Choi, J. Cho, "High-performance of Deep Learning Colorization with Wavelet Fusion," IEMEK J. Embed. Sys. Appl., Vol. 13, No. 6, pp. 313-319, 2018 (in Korean). https://doi.org/10.14372/IEMEK.2018.13.6.313
  10. K. He, J. Sun, X. Tang, "Single Image Haze Removal Using Dark Channel Prior," Journal of IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 33, No. 12, pp. 2341-2353, 2011. https://doi.org/10.1109/TPAMI.2010.168