SpringerLink
Log in

A laser-assisted depth detection method for underwater monocular vision

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Underwater target three-dimensional detection is crucial for effectively recognizing and acquiring target information in complex water rings. The underwater robotic operating system as a conventional underwater operating platform, generally equipped with a binocular or monocular camera, how to utilize the underwater monocular camera with high precision and high efficiency to complete the target three-dimensional information acquisition is the main research starting point of this paper. To this end, this paper proposes a laser-assisted three-dimensional depth monocular detection method for underwater targets, which utilizes three cross lasers to assist the monocular camera system in capturing the depth data at different positions of the target plane at one time. The image correction by the four-point laser calibration method in this paper solves the difficulties of image distortion caused by an unstable underwater environment and lens effect, as well as laser angle deviation caused by the tilting of the underwater robot. The instability of the underwater environment and the lens can cause image distortion, and the tilt of the underwater robot causes the laser angle to deviate. After correcting the image, the depth data between the target and the robot can be calculated based on the geometric relationship that exists between the imaginary rectangle formed by the laser dots and laser lines in the image and the imaginary rectangle formed between the lasers on the device. This method uses a single image to obtain target depth information and is capable of measuring not only horizontal planes but also multiplanes and inclined planes. Experiments show that the algorithm improves the performance accuracy in underwater environments and land environments compared to traditional methods, and obtains depth information for the entire plane at once. The method provides a theoretical and practical basis for underwater monocular 3D information acquisition.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (Canada)

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

Similar content being viewed by others

Data availability

Data available on request from the authors.

References

  1. Yoerger DR, Jakuba M, Bradley AM, Bingham B (2007) Techniques for deep sea near bottom survey using an autonomous underwater vehicle. Int J Robot Res 26(1):41–54

    Article  Google Scholar 

  2. Wu Y, Ta X, **ao R, Wei Y, An D, Li D (2019) Survey of underwater robot positioning navigation. Appl Ocean Res 90:101845

    Article  Google Scholar 

  3. Faugeras OD, Luong QT, Maybank SJ (1992) Camera self-calibration: Theory and experiments. In Computer Vision—ECCV'92: Second European Conference on Computer Vision Santa Margherita Ligure, Italy, May 19–22, 1992 Proceedings 2 (321–334). Springer Berlin Heidelberg

  4. Tsai DM, Chiang CH (2002) Rotation-invariant pattern matching using wavelet decomposition. Pattern Recogn Lett 23(1–3):191–201

    Article  Google Scholar 

  5. Zhang Z (2004) Camera calibration with one-dimensional objects. IEEE Trans Pattern Anal Mach Intell 26(7):892–899

    Article  Google Scholar 

  6. McIvor AM (2002) Nonlinear calibration of a laser stripe profiler. Opt Eng 41(1):205–212

    Article  Google Scholar 

  7. Reid ID (1996) Projective calibration of a laser-stripe range finder. Image Vis Comput 14(9):659–666

    Article  Google Scholar 

  8. Tiddeman B, Duffy N, Rabey G, Lokier J (1998) Laser-video scanner calibration without the use of a frame store. IEE Proc-Vision Image Signal Process 145(4):244–248

    Article  Google Scholar 

  9. Reshetyuk Y (2010) A unified approach to self-calibration of terrestrial laser scanners. ISPRS J Photogramm Remote Sens 65(5):445–456

    Article  Google Scholar 

  10. Gassner G, Ruland R (2008) Laser tracker calibration-testing the angle measurement system (No. SLAC-PUB-13476). SLAC National Accelerator Lab., Menlo Park, CA (United States)

  11. Metoyer S, Bogucki D (2021) Underwater laser imaging. Polish Hyperbaric Res 77(4):39–52

    Article  Google Scholar 

  12. Kun L, Su-Hui Y, Ying-Qi L, Xue-Tong L, **n W, **-Ying Z, Zhuo L (2021) Underwater ranging with intensity modulated 532 nm laser source. Acta Physica Sinica 70(8)

  13. Li S, Yang X (2017) The research of binocular vision ranging system based on LabVIEW. In AIP Conference Proceedings 1890(1) 040056. AIP Publishing LLC

  14. Sun X, Jiang Y, Ji Y, Fu W, Yan S, Chen Q., ... Gan X (2019) Distance measurement system based on binocular stereo vision. In IOP Confer Ser: Earth and Environmental Science 252(5) 052051 IOP Publishing

  15. Wang Q, Zhang Y, Shi W, Nie M (2020) Laser ranging-assisted binocular visual sensor tracking system. Sensors 20(3):688

    Article  Google Scholar 

  16. Fang, Z., Lin, T., Li, Z., Yao, Y., Zhang, C., Ma, R., ... & Ren, H. (2022). Automatic Walking Method of Construction Machinery Based on Binocular Camera Environment Perception. Micromachines 13(5) 671

  17. Guo S, Chen S, Liu F, Ye X, Yang H (2017) Binocular vision-based underwater ranging methods. In 2017 IEEE International Conference on Mechatronics and Automation (ICMA) 1058–1063 IEEE

  18. Huo G, Wu Z, Li J, Li S (2018) Underwater target detection and 3D reconstruction system based on binocular vision. Sensors 18(10):3570

    Article  Google Scholar 

  19. Wu X, Tang X (2019) Accurate binocular stereo underwater measurement method. Int J Adv Rob Syst 16(5):1729881419864468

    MathSciNet  Google Scholar 

  20. He L, Yang J, Kong B, Wang C (2017) An automatic measurement method for absolute depth of objects in two monocular images based on SIFT feature. Appl Sci 7(6):517

    Article  Google Scholar 

  21. Jiafa M, Wei H, Weiguo S (2020) Target distance measurement method using monocular vision. IET Image Proc 14(13):3181–3187

    Article  Google Scholar 

  22. Yuan F, He J (2020) Human height measurement in surveillance video based on vision technology. Int Core J Eng 6(5):198–208

    Google Scholar 

  23. Huang L, Wu G, Tang W, Wu Y (2021) Obstacle distance measurement under varying illumination conditions based on monocular vision using a cable inspection robot. IEEE Access 9:55955–55973

    Article  Google Scholar 

  24. Xue L, Li M, Fan L, Sun A, Gao T (2021) Monocular Vision Ranging and Camera Focal Length Calibration. Sci Program 2021:1–15

    Google Scholar 

  25. Lang J, Mao J, Liang R (2022) Non-horizontal target measurement method based on monocular vision. Syst Sci Control Eng 10(1):443–458

    Article  Google Scholar 

  26. Wu G, Zeng L (2007) Video tracking method for three-dimensional measurement of a free-swimming fish. Sci China Ser G 50(6):779–786

    Article  Google Scholar 

  27. Hemelrijk CK, Hildenbrandt H, Reinders J, Stamhuis EJ (2010) Emergence of oblong school shape: models and empirical data of fish. Ethology 116(11):1099–1112

    Article  Google Scholar 

  28. Mao J, **ao G, Sheng W, Qu Z, Liu Y (2016) Research on realizing the 3D occlusion tracking location method of fish’s school target. Neurocomputing 214:61–79

    Article  Google Scholar 

  29. Chi S, **e Z, Chen W (2016) A laser line auto-scanning system for underwater 3D reconstruction. Sensors 16(9):1534

    Article  Google Scholar 

  30. Xue Q, Sun Q, Wang F, Bai H, Yang B, Li Q (2021) Underwater high-precision 3D reconstruction system based on rotating scanning. Sensors 21(4):1402

    Article  Google Scholar 

  31. Singh D, Kaur M, Jabarulla MY, Kumar V, Lee HN (2022) Evolving fusion-based visibility restoration model for hazy remote sensing images using dynamic differential evolution. IEEE Trans Geosci Remote Sens 60:1–14

    Google Scholar 

Download references

Funding

The author disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by Natural Science Foundation of Shanghai (No.14ZR1414900) for providing financial support for this work.

Author information

Authors and Affiliations

  1. School of Mechatronic Engineering and Automation, Shanghai University, No. 99 Shangda Road, Shanghai, China

    Zhijie Tang, Congqi Xu & Siyu Yan

Authors
  1. Zhijie Tang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Congqi Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Siyu Yan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zhijie Tang.

Ethics declarations

Competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Tang, Z., Xu, C. & Yan, S. A laser-assisted depth detection method for underwater monocular vision. Multimed Tools Appl 83, 64683–64716 (2024). https://doi.org/10.1007/s11042-024-18167-2

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-024-18167-2

Keywords

Search

Navigation