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)


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.


Inland river Kalman filter Overloaded ship identification 



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).


  1. 1.
    Lee E-K, Ho Y-S (2010) Generation of multi-view video using a fusion camera system for 3D displays. IEEE Trans Consum Electron 56(11):2797–2805CrossRefGoogle Scholar
  2. 2.
    Ha DM, Lee JM, Kim YD (2004) Neural-edge-based ship detection and traffic parameter extraction. Image Vis Comput 22(6): 899–907Google Scholar
  3. 3.
    Hu H, Tian J, Dai G, Wang M, Peng Y (2011) A new method of ship detection for SAR images. Int J Advancements Comput Technol 3(9):64–71CrossRefGoogle Scholar
  4. 4.
    Wu J, Mao S, Wang X, Zhang T (2011) Ship target detection and tracking in cluttered infrared imagery. Opt Eng 50(5):234–247CrossRefGoogle Scholar
  5. 5.
    Obad D, Bošnjak-Cihlar Z (2004) Benefits of automatic identification system within framework of river information services. In: Proceedings Elmar—International Symposium Electronics in Marine Proceedings, vol 46(23), pp 143–147 Google Scholar
  6. 6.
    Vicen-Bueno R, Carrasco-álvarez R, Jarabo-Amores MP, Nieto-Borge JC, Rosa-Zurera M (2011) Ship detection by different data selection templates and multilayer perceptrons from incoherent maritime radar data. IET Radar Sonar Navig 5(2):144–154CrossRefGoogle Scholar
  7. 7.
    Ruiz ARJ, Granja FS (2009) A short-range ship navigation system based on ladar imaging and target tracking for improved safety and efficiency. IEEE Trans Intell Transp Syst 10(1):186–197CrossRefGoogle Scholar
  8. 8.
    Zhu C, Zhou H, Wang R, Guo J (2010) A novel hierarchical method of ship detection from spaceborne optical image based on shape and texture features. IEEE Trans Geosci Remote Sens 48(9):3446–3456CrossRefGoogle Scholar
  9. 9.
    Li P, Zhang T, Ma B (2004) Unscented kalman filter for visual curve tracking. Image Vis Comput 22(2):157–164CrossRefGoogle Scholar
  10. 10.
    Piovoso MP, Laplante PA (2003) Kalman filter recipes for real-time image processing. Real-Time Imag 9(6):433–439CrossRefGoogle Scholar

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

Personalised recommendations