Skip to main content
Log in

Acoustic beam profile-based rapid underwater object detection for an imaging sonar

  • Original article
  • Published:
Journal of Marine Science and Technology Aims and scope Submit manuscript

Abstract

In sonar applications, the ability to locate underwater structures such as pipelines and a wreckage of submerged airplane is important. To investigate extensive sections of the seabed within a limited time period, the scanning speed and the reliability of object detection alarms are the most critical factors for finding objects. In this paper, we propose a method to provide an automatic detection alarm indicating the presence of suspected underwater objects using high-speed imaging sonar. The proposed method is based on the cross-correlations between two successive acoustic beam profiles of imaging sonar. The alarm signal alerts human operators or automatic underwater vehicles to suspected objects, which may be a part of or all of the target object. Using this signal as a trigger, the object can then be examined in more detail to determine whether it is the target. We verified the feasibility of the proposed method by indoor and field experiments.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22
Fig. 23
Fig. 24
Fig. 25
Fig. 26
Fig. 27
Fig. 28
Fig. 29
Fig. 30

Similar content being viewed by others

References

  1. Zingaretti P, Zanoli SM (1998) Robust real-time detection of an underwater pipeline. Eng Appl Artif Intell 11:257–268

    Article  Google Scholar 

  2. Nasahashi K, Ura T, Asada A, Obara T, Sakamaki T, Kim K, Okamura K (2005) Underwater volcano observation by autonomous underwater vehicle “r2D4”. Oceans Eur 1:557–562

    Google Scholar 

  3. Kondo H, Ura T (2004) Navigation of an AUV for investigation of underwater structure. Control Eng Pract 12:1551–1559

    Article  Google Scholar 

  4. Purcell M, Gallo D, Packard G, Dennett M, Rothenbeck M, Sherrell A, Pascaud S (2011) Use of REMUS 6000 AUVs in the search for the Air France Flight 447. Oceans 2011:1–7

    Google Scholar 

  5. Daniel S, Lèannec FL, Roux C, Solaiman B, Maillard EP (1998) Side-scan sonar image matching. IEEE J Ocean Eng 23:245–259

    Article  Google Scholar 

  6. Mignotte M, Collet C, Pèrez P, Bouthemy P (2000) Sonar image segmentation using an unsupervised hierarchical MRF model. IEEE Trans Image Process 9:1216–1231

    Article  Google Scholar 

  7. Reed S, Petillot Y, Bell J (2003) An automatic approach to the detection and extraction of mine features in sidescan sonar. IEEE J Ocean Eng 28:90–105

    Article  Google Scholar 

  8. Belcher EO, Lynn DC (2000) Acoustic, near-video-quality images for work in turbid water. In: Proceedings of underwater intervention 2000 conference, pp 187–192

  9. Yu SC (2008) Development of real-time acoustic image recognition system using by autonomous marine vehicle. Ocean Eng 35:90–105

    Article  Google Scholar 

  10. Negahdaripour S, Firoozfam P, Sabzmeydani P (2005) On processing and registration of forward-scan acoustic video imagery. In: Proceedings of the 2nd Canadian conference on computer and robot vision, pp 452–459

  11. Yu SC, Kim TW, Weatherwax S, Collins B, Yuh J (2006) Development of high-resolution acoustic camera based real-time object recognition system by using autonomous underwater vehicles. Oceans 2006:1–6

    MATH  Google Scholar 

  12. Aykin MD, Negahdaripour S (2012) On feature extraction and region matching for forward scan sonar imaging. Oceans 2012:1–9

    Google Scholar 

  13. Lee Y, Kim TG, Choi H-T (2013) Preliminary study on a framework for imaging sonar based underwater object recognition. In: Proceedings of 10th international conference on ubiquitous robots and ambient, intelligence, pp 517–520

  14. Gu J-H, Joe HG, Yu SC (2013) Development of image sonar simulator for underwater object recognition, Oceans, San Diego, pp 1–6

  15. Petillot Y, Tena Ruiz I, Lane DM (2001) Underwater vehicle obstacle avoidance and path planning using a multi-beam forward looking sonar. IEEE J Ocean Eng 26:240–251

    Article  Google Scholar 

  16. Caimi FM, Kocak DM, Dalgleish F, Watson J (2008) Underwater imaging and optics: recent advances. Oceans 2008:1–9

    Google Scholar 

  17. Chaillan F, Fraschini C, Courmontagne P (2007) Speckle noise reduction in SAS imagery. Signal Process 87:762–781

    Article  MATH  Google Scholar 

  18. DIDSON website. www.soundmetrics.com. Accessed 6 Nov 2014

  19. Orfanidis SJ (1996) Optimum signal processing: an introduction, 2nd edn. Prentice Hall, Englewood Cliffs

    Google Scholar 

Download references

Acknowledgments

This research was supported by the Ministry of Science, ICT and Future Planning, Korea, under the IT Consilience Creative Program (NIPA-2014-H0201-14-1001), supervised by the NIPA National IT Industry Promotion Agency. This work was partly supported by the Civil Military Technology Cooperation Center. This research was a part of the project titled “Gyeongbuk Sea Grant Program”, funded by the Ministry of Oceans and Fisheries, Korea.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Son-Cheol Yu.

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Cho, H., Gu, J., Joe, H. et al. Acoustic beam profile-based rapid underwater object detection for an imaging sonar. J Mar Sci Technol 20, 180–197 (2015). https://doi.org/10.1007/s00773-014-0294-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00773-014-0294-x

Keywords

Navigation