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http://dx.doi.org/10.12989/ose.2020.10.4.435

Sonar-based yaw estimation of target object using shape prediction on viewing angle variation with neural network  

Sung, Minsung (Department of IT Engineering, Pohang University of Science and Technology (POSTECH))
Yu, Son-Cheol (Department of IT Engineering, Pohang University of Science and Technology (POSTECH))
Publication Information
Ocean Systems Engineering / v.10, no.4, 2020 , pp. 435-449 More about this Journal
Abstract
This paper proposes a method to estimate the underwater target object's yaw angle using a sonar image. A simulator modeling imaging mechanism of a sonar sensor and a generative adversarial network for style transfer generates realistic template images of the target object by predicting shapes according to the viewing angles. Then, the target object's yaw angle can be estimated by comparing the template images and a shape taken in real sonar images. We verified the proposed method by conducting water tank experiments. The proposed method was also applied to AUV in field experiments. The proposed method, which provides bearing information between underwater objects and the sonar sensor, can be applied to algorithms such as underwater localization or multi-view-based underwater object recognition.
Keywords
sonar GAN; underwater GAN; object detection; sonar simulator; underwater sonar image; underwater object detection; acoustic landmark;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
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1 Isola, P., Zhu, J.Y., Zhou, T. and Efros, A.A. (2017), "Image-to-image translation with conditional adversarial networks", Proceedings of the IEEE conference on computer vision and pattern recognition.
2 Johannsson, H., Kaess, M., Englot, B., Hover, F. and Leonard, J. (2010), "Imaging sonar-aided navigation for autonomous underwater harbor surveillance", Proceedings of the 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems.
3 Karimanzira, D., Renkewitz, H., Shea, D. and Albiez, J. (2020), "Object Detection in Sonar Images", Electronics, 9(7), 1180.   DOI
4 Kim, B. and Yu, S.C. (2017), "Imaging sonar based real-time underwater object detection utilizing adaboost method", In 2017 IEEE Underwater Technology (UT), 1-5
5 Kim, J., Kim, T., Kim, J., Rho, S., Song, Y.W. and Yu, S.C. (2019), "Simulation and Feasibility Test of MiniROVs with AUV for the Manipulation Purpose", In OCEANS 2019 MTS/IEEE SEATTLE, 1-6.
6 Kim, J., Sung, M. and Yu, S.C. (2018a), "Development of simulator for autonomous underwater vehicles utilizing underwater acoustic and optical sensing emulators", Proceedings of the 2018 18th International Conference on Control, Automation and Systems (ICCAS).
7 Kim, T., Kim, J. and Byun, S.W. (2018b), "A comparison of nonlinear filter algorithms for terrain-referenced underwater navigation", Int. J. Control Autom. Syst., 16(6), 2977-2989.   DOI
8 Lee, M., Kim, J. and Yu, S.C. (2019), "Robust 3D Shape Classification Method using Simulated Multi View Sonar Images and Convolutional Nueral Network", In OCEANS 2019-Marseille, 1-5.
9 Loc, M.B., Choi, H.S., Seo, J.M., Baek, S.H. and Kim, J.Y. (2014), "Development and control of a new AUV platform", Int. J. Control, Autom. Syst., 12(4), 886-894.   DOI
10 Maki, T., Horimoto, H., Ishihara, T. and Kofuji, K. (2020), "Tracking a Sea Turtle by an AUV with a Multibeam Imaging Sonar: Toward Robotic Observation of Marine Life", Int. J. Control Autom. Syst., 18(3), 597-604.   DOI
11 Myers, V. and Williams, D.P. (2011), "Adaptive multiview target classification in synthetic aperture sonar images using a partially observable Markov decision process", IEEE J. Oceanic Eng., 37(1), 45-55.   DOI
12 Palmese, M. and Trucco, A. (2006), "Acoustic imaging of underwater embedded objects: Signal simulation for three-dimensional sonar instrumentation", IEEE T. Instrum. Measurement, 55(4), 1339-1347.   DOI
13 Ronneberger, O., Fischer, P. and Brox, T. (2015), "U-net: Convolutional networks for biomedical image segmentation", Proceedings of the International Conference on Medical image computing and computerassisted intervention.
14 Tang, Z., Wang, Z., Lu, J., Ma, G. and Zhang, P. (2019), "Underwater robot detection system based on fish's lateral line", Electronics, 8(5), 566.   DOI
15 Yu, S.C. (2008), "Development of real-time acoustic image recognition system using by autonomous marine vehicle", Ocean Eng., 35(1), 90-105.   DOI
16 Hong, S. and Kim, J. (2020), "Three-dimensional Visual Mapping of Underwater Ship Hull Surface Using Piecewise-planar SLAM", Int. J. Control Autom. Syst., 18(3), 564-574.   DOI
17 Belcher, E., Hanot, W. and Burch, J. (2002), "Dual-frequency identification sonar (DIDSON)", Proceedings of the 2002 Interntional Symposium on Underwater Technology (Cat. No. 02EX556).
18 Cho, H., Gu, J. and Yu, S.C. (2015), "Robust sonar-based underwater object recognition against angle-of-view variation", IEEE Sensor. J., 16(4), 1013-1025.   DOI
19 Etter, P.C. (1995), "Underwater acoustic modeling: principles, techniques and applications", CRC Press.