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A New Method of Inland River Overloaded Ship Identification Using Digital Image Processing

  • Lei Xie
  • Jing Chen
  • Zhongzhen Yan
  • Zheyue Wang
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 220)

Abstract

With the development of low-carbon economy, inland river transportation has been attracting more and more attention in China. At the same time, driven by some economic benefits, the ship overload phenomenon continues to occur. Therefore, overloaded ship detection has been a key factor for reducing marine traffic accidents. This paper presents a robust method for detecting overloaded ship and the proposed algorithm includes three stages: ship detection, ship tracking, and overloaded ship identification. Ship detection is a key step and the concept of ship tracking is built on the ship-segmentation method. According to the segmented ship shape, we propose a predict method based on Kalman filter to track each ship. The data of ship length and ship speed will be used to identify overloaded ship. The proposed method has been tested on a number of monocular ship image sequences and the experimental results show that the algorithm is robust and real-time.

Keywords

Inland river Kalman filter Overloaded ship identification 

Notes

Acknowledgments

The author thanks his colleagues for their influence. Thanks to the referees for their suggestions which have greatly improved the presentation of the paper. This work was supported by Transportation Construction Technology Project (201132820190) and Department of Transportation Industry tackling Project (2009353460640).

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Copyright information

© Springer-Verlag London 2013

Authors and Affiliations

  • Lei Xie
    • 1
    • 2
  • Jing Chen
    • 1
    • 2
  • Zhongzhen Yan
    • 1
    • 2
  • Zheyue Wang
    • 1
    • 2
  1. 1.Engineering Research Center for Transportation Safety (Ministry of Education)Wuhan University of TechnologyWuhanChina
  2. 2.Intelligent Transport System Research CenterWuhan University of TechnologyWuhanChina

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