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http://dx.doi.org/10.14372/IEMEK.2020.15.2.71

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

Kim, Hanguen (Seadronix Corp.)
Kim, Donghoon (Seadronix Corp.)
Park, Byeolteo (Seadronix Corp.)
Lee, Seung-Mok (Keimyung University)
Publication Information
IEMEK Journal of Embedded Systems and Applications / v.15, no.2, 2020 , pp. 71-78 More about this Journal
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
Around view monitoring; Deep learning; Docking assist system; Ship berthing aid system; Vision sensor;
Citations & Related Records
Times Cited By KSCI : 4  (Citation Analysis)
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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.   DOI
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).   DOI
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).   DOI
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.   DOI