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A object detection and tracking method for security in intelligence of unmanned surface vehicles

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Abstract

Unmanned surface vehicle is increasingly becoming a research hotspot, which can be used in a variety of civil and military missions. However, compared with the relative maturity of other technologies, the sensing technology of unmanned surface vehicles is relatively weak. Taking "WAM-V-USV" as the research platform, this paper is mainly focus on the detection and tracking methods of moving objects with unmanned surface vehicles. This paper introduces the environment sensing system of unmanned vehicle, water surface image preprocessing, water antenna detection based on SVM, and the method of water surface object detection and tracking based on improved YOLOV3. The simulation results show that the proposed method can effectively improve the accuracy of moving object detection and tracking. Through the practical application in the Songhua River and the US Unmanned Surface Vehicles Open, it is proved that the algorithm has a good detection and tracking effect and meets the real-time requirements. Practice has proved that the object detection and tracking method based on deep learning greatly improves the perception ability and self-security of the unmanned surface vehicles.

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Funding

This paper is supported by Development Program of China via Grants 2018YFC0806802 and 2018YFC0832105.

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Authors and Affiliations

  1. Department of Mechanical and Electrical Engineering, Harbin Institute of Technology, Harbin, 150001, China

    Wei Zhang & Chi-fu Yang

  2. National University of Defense Technology, Changsha, Hunan, China

    Xian-zhong Gao

  3. Department of Computer Science and Technology, Harbin Institute of Technology, Harbin, China

    Feng Jiang

  4. School of Computer Science, University of Nottingham, Kuala Lumpur, Malaysia

    Zhi-yuan Chen

Authors
  1. Wei Zhang
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  2. Xian-zhong Gao
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  3. Chi-fu Yang
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  4. Feng Jiang
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  5. Zhi-yuan Chen
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Contributions

WZ is responsible for the overall structure of the paper; X-zG and C-fY are provide experimental guidance; FJ and Z-yC are responsible for providing datasets and test platforms.

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Correspondence to Wei Zhang.

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Zhang, W., Gao, Xz., Yang, Cf. et al. A object detection and tracking method for security in intelligence of unmanned surface vehicles. J Ambient Intell Human Comput 13, 1279–1291 (2022). https://doi.org/10.1007/s12652-020-02573-z

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  • DOI: https://doi.org/10.1007/s12652-020-02573-z

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