Robust Object Detection in Sea Environment Based on DWT

  • Jongmyeon Jeong
  • Ki Tae Park
  • Gyei-Kark Park
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 272)


In this paper, a new method for detecting various objects that can be risks to safety navigation in sea environment is proposed. By analyzing Infrared(IR) images obtained from various sea environments, we could find out that object regions include both horizontal and vertical direction edges while background regions of sea surface mainly include vertical direction edges. Therefore, we present an approach to detecting object regions considering horizontal and vertical edges. To this end, in the first step, image enhancement is performed by suppressing noises such as sea glint and complex clutters using a statistical filter. In the second step, a horizontal edge map and a vertical edge map are generated by Discrete Wavelet Transform. Then, a saliency map integrating the horizontal and the vertical edge maps is generated. Finally, object regions are detected by an adaptive thresholding method.


safety navigation horizontal edge map vertical edge map discrete wavelet transform object region detection 


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  1. 1.
    Kim, D.J., Kwak, S.Y.: Evaluation of Human Factors in Ship Accedents in the Domestic Sea. Journal of the Ergonomics Society of Korea 30(1), 87–98 (2011)CrossRefGoogle Scholar
  2. 2.
    FaulKemer, D.: Shipping Safety. Ingenia (2003)Google Scholar
  3. 3.
    Toffoli, A., Lefevra, J.M., Bitner-Gregersen, E., Monbaliu, J.: Toward the Identification of Warning Criteria: Analysis of a Ship Accident Database. Journal of Applied Ocean Research 27, 281–291 (2005)CrossRefGoogle Scholar
  4. 4.
    Jeong, J., Park, G.-K.: Object Detection Algorithm Using Edge Information on the Sea Environment. Journal of the Korea Society of Computer and Information 16(9), 69–76 (2011)CrossRefMathSciNetGoogle Scholar
  5. 5.
    Gaarder, S., Rongstad, K., Olofsson, M.: Image of human elements in marine risk management. Guedes Soares C., Advances in safety and reliability, pp. 857–898. Pergarmon (1997)Google Scholar
  6. 6.
    Vicker, V.E.: Plateu equalization algorithm for real -time display of high-quality infrared imagery. Optical Engineering 35(7), 1921–1926 (1996)CrossRefGoogle Scholar
  7. 7.
    Brustrom, K., et al.: Object detection in cluttered infrared images. Optical Engineering 42(2), 388–399 (2003)CrossRefGoogle Scholar
  8. 8.
    Gonzalez, R.C., Woods, R.E.: Digital image precessing, 2nd edn. Prentice Hall (2001)Google Scholar
  9. 9.
    Barni, M.: Document and Image Compression. CRS Press, Taylor and Francis Group (2006)Google Scholar
  10. 10.
    Nguyen, T.Q.: A tutorial on ¯lter banks and wavelets. University of Wisconsin, ECE Department (June 1995)Google Scholar
  11. 11.
    Bovik, A.: The Essentuial Guide to Image Processing, 2nd edn. Elsevier, Inc. (2009)Google Scholar
  12. 12.
    Ostu, N.: A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man, Cybernetics SMC-9, 62–66 (1979)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Jongmyeon Jeong
    • 1
  • Ki Tae Park
    • 2
  • Gyei-Kark Park
    • 1
  1. 1.Department of Computer EngineeringMokpo National Maritime UniversityMokpo-siSouth Korea
  2. 2.Center for Integrated General EducationHanyang UniversitySeoulSouth Korea

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