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ISSN (Print): 2352-0965
ISSN (Online): 2352-0973

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Research Article

Water Boundary Line Detection for Unmanned Surface Vehicles

Author(s): Yuejun Liu, Liyong Ma*, Wei Xie, Xiaolei Zhang and Yong Zhang

Volume 13, Issue 8, 2020

Page: [1145 - 1152] Pages: 8

DOI: 10.2174/2352096513999200519075215

Price: $65

Abstract

Background: Unmanned Surface Vehicles (USV) can undertake risks or special tasks in marine independently and will be widely used in the future. In the autonomous navigation of USV equipped with vision camera, the water boundary line needs to be detected in real time and it is one of these key intelligent environment perception methods for USV.

Methods: An efficient water boundary line detection method based on Gray Level Co-occurrence Matrix (GLCM) texture entropy is proposed. In image preprocessing, the high-brightness areas are eliminated to avoid the effects of water boundary line detection.

Results: GLCM entropy is employed to segment water, land and air for water line regression. The proposed method is efficient for the images with high-brightness areas.

Conclusion: The experimental results demonstrate that the proposed method is not only more accurate than the existing water boundary line detection method, but also has good real-time performance and is suitable for the application in USV.

Keywords: Gray level co-occurrence matrix, image processing, texture entropy, unmanned surface vehicles (USV), water boundary line detection, canny and hough transform.

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[1]
M. Schiaretti, L. Chen, and R. Negenborn, Survey on autonomous surface vessels: Part I - a new detailed definition of autonomy levels Lect. Notes Comput. Sci., , vol. 10572. pp. 219-233. 2017
[http://dx.doi.org/10.1007/978-3-319-68496-3_15]
[2]
D. Nad, N. Miskovic, and F. Mandic, "Navigation, guidance and control of an overactuated marine surface vehicle", Annu. Rev. Contr., vol. 40, no. 1, pp. 172-181, 2015.
[http://dx.doi.org/10.1016/j.arcontrol.2015.08.005]
[3]
Y. Liu, and R. Bucknall, "Path planning algorithm for unmanned surface vehicle formations in a practical maritime environment", Ocean Eng., vol. 97, pp. 126-144, 2015.
[http://dx.doi.org/10.1016/j.oceaneng.2015.01.008]
[4]
L. Ma, "Support tucker machines based marine oil spill detection using SAR images", Indian J. Geo-Mar. Sci., vol. 45, no. 11, pp. 1445-1449, 2016.
[5]
S. Ferretti, "Unmanned systems present new opportunities", Int. Water Power Dam Constr., vol. 70, no. 4, pp. 28-30, 2018.
[6]
S. Jia, L. Ma, and S. Zhang, "Big data prototype practice for unmanned surface vehicle", Proc. International Conference on Communication and Information Processing Qingdao, China, pp. 43-47, 2018.
[http://dx.doi.org/10.1145/3290420.3290466]
[7]
L. Ma, W. Xie, and Y. Zhang, "Blister defect detection based on convolutional neural network for polymer lithium-ion batterys", Appl. Sci. (Basel), vol. 9, no. 6, pp. 1086-1096, 2019.
[http://dx.doi.org/10.3390/app9061085]
[8]
L. Ma, C. Ma, Y. Liu, and X. Wang, "Thyroid diagnosis from SPECT images using convolutional neural network with optimization", Comput. Intell. Neurosci., vol. 2019, no. 1, 2019.
[http://dx.doi.org/10.1155/2019/6212759 PMID: 30766599]
[9]
L. Ma, L. Tang, W. Xie, and S. Cai, "Ship detection in SAR using extreme learning machine: Lecture Notes of the Institute for Computer Science", Social Inform. Telecommun. Eng., vol. 227, no. 1, pp. 558-569, 2018.
[10]
Y. Liu, L. Ma, W. Xie, and X. Jia, "Parallel GPU computation model for block matching of speckle tracing", J. Nonlinear Convex Anal., vol. 20, no. 5, pp. 827-834, 2019.
[11]
L.Y. Ma, W. Xie, and H.B. Huang, "Convolutional neural network based obstacle detection for unmanned surface vehicle", Math. Biosci. Eng., vol. 17, no. 1, pp. 845-861, 2019.
[http://dx.doi.org/10.3934/mbe.2020045 PMID: 31731381]
[12]
L. Piancastelli, L. Frizziero, S. Marcoppido, A. Donnarumma, and E. Pezzuti, "Fuzzy control system for recovering direction after spinning", Int. J. Heat Technol., vol. 29, no. 2, pp. 87-93, 2011.
[13]
L. Piancastelli, L. Frizziero, S. Marcoppido, A. Donnarumma, and E. Pezzuti, "Active antiskid system for handling improvement in motorbikes controlled by fuzzy logic", Int. J. Heat Technol., vol. 29, no. 2, pp. 95-101, 2011.
[14]
J. Park, M. Kang, and T. Kim, "Development of an unmanned surface vehicle system for the 2014 maritime robot X challenge", J. Field Robot., vol. 34, no. 4, pp. 644-665, 2017.
[http://dx.doi.org/10.1002/rob.21659]
[15]
Y. Wei, and Y. He, "Automatic water line detection for an USV system", Proc. IEEE International Conference on Cyber Technology in Automation, Control and Intelligent Systems Chengdu, China, pp. 261-266, 2016.
[http://dx.doi.org/10.1109/CYBER.2016.7574833]
[16]
M. Silveira, "Separation between water and land in SAR images using region-based level sets", IEEE Geosci. Remote Sens. Lett., vol. 6, no. 3, pp. 471-475, 2009.
[http://dx.doi.org/10.1109/LGRS.2009.2017283]
[17]
Y. Yu, and S.T. Acton, "Automated delineation of coastline from polarimetric SAR imagery", Int. J. Remote Sens., vol. 25, no. 17, pp. 3423-3438, 2004.
[http://dx.doi.org/10.1080/0143116032000160444]
[18]
H. Dong, P. Xu, Q. Liu, and H. Xu, "The water coastline detection approaches based on USV vision", Proc. IEEE International Conference on Cyber Technology in Automation, Control and Intelligent Systems Shenyang, China, pp. 404-408, 2015.
[19]
H. Guo, Y. Zhang, J. Zhou, and Y. Zhang, "A fast and robust vision-based horizon tracking method", Proc. International Computer Conference on Wavelet Active Media Technology and Information Processing Chengdu, China, pp. 71-74, 2015.
[20]
N.S. Boroujeni, S.A. Etemad, and A. Whitehead, "Robust horizon detection using segmentation for UAV applications", Proceeding of Conference on Computer and Robot Vision Toronto, Canada, pp. 28-30, 2012.
[http://dx.doi.org/10.1109/CRV.2012.52]
[21]
L. Su, D. Pang, and Z. Liu, "Panoramic sea-sky line extraction based on improved Hough circle transform", Proc. Chinese Control Conf. Hangzhou, China, pp. 3690-3694, 2015.

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