Skip to main content
Log in

Texture feature-based local adaptive Otsu segmentation and Hough transform for sea-sky line detection

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

Abstract

The sea-sky line is an important basis for marine unmanned equipment to perceive the sea environment. However, due to the interference of many external factors, such as sea fog, illumination, and sea target occlusion, sea-sky line detection is challenging. Therefore, we present a texture feature-based local adaptive Otsu segmentation and Hough transform for sea-sky line detection. In this method, image texture features and weighted texture quantization are used to determine the sea-sky area. Based on this sea-sky area, a longitudinal block strategy is introduced, and a new adaptive Otsu segmentation method is applied to obtain the binary image of the sea-sky area. The obtained binary image is then post-processed to enhance the sea-sky line edge information. On this basis, a new adaptive Canny edge detector is applied, and the sea-sky line is extracted by the Hough transform. The experimental results show that the proposed sea-sky line detection method has high accuracy and robustness when handling images of complex marine environments. Compared with other algorithms, the detection error and the error standard deviation of the proposed method are relatively small, indicating that this method is more stable and accurate than other algorithms.

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.

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

Similar content being viewed by others

Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

References

  1. Jiang C, Jiang H, Zhang C et al (2010) A new method of sea-sky-line detection. In: 2010 Third International Symposium on Intelligent Information Technol Secur Inf 740–743. https://doi.org/10.1109/IITSI.2010.147

  2. Pu H, Bo L, Ren T, Cai Y (2014) A fast sea-level line extraction and object detection method for infrared sea image. In: International Symposium on Optoelectronic Technology Application: Infrared Technology Applications 9300. https://doi.org/10.1117/12.2074406

  3. Lin C, Chen W, Zhou H (2020) Multi-visual feature saliency detection for sea-surface targets through improved sea-sky-line detection. J Mar Sci Eng 8(10):799–814. https://doi.org/10.3390/jmse8100799

    Article  MathSciNet  Google Scholar 

  4. Ma T, Ma J, Fu W (2016) Sea-sky line extraction with linear fitting based on line segment detection. In: 2016 9th International Symposium on Computational Intelligence and Design (ISCID) 1, 46–49. https://doi.org/10.1109/ISCID.2016.1019

  5. Da T, Sun G, Niu Z (2013) Research on infrared ship detection method in sea-sky background. In: Society of Photo-optical Instrumentation Engineers 8907. https://doi.org/10.1117/12.2033039

  6. Liu J, Li H, Liu J et al (2021) Real-time monocular obstacle detection based on horizon line and saliency estimation for unmanned surface vehicles. Mobile Netw Appl 26(3):1372–1385. https://doi.org/10.1007/s11036-021-01752-2

    Article  Google Scholar 

  7. Fefilatyev S, Goldgof D, Shreve M et al (2012) Detection and tracking of ships in open sea with rapidly moving buoy-mounted camera system. Ocean Eng 54:1–12. https://doi.org/10.1016/j.oceaneng.2012.06.028

    Article  Google Scholar 

  8. Prasad DK, Rajan D, Prasath CK et al (2016) Mscm-life: Multi-scale cross modal linear feature for horizon detection in maritime images. In: 2016 IEEE Region 10 Conference (TENCON) 1366–1370. https://doi.org/10.1109/TENCON.2016.7848237

  9. Xu L, Ma L, Xie X (2017) Sea-sky line detection based on structured forests edge detection and hough transform. J Shanghai Univ 23(1):47–55

    Google Scholar 

  10. Liu S, Zhou X, Wang C (2006) Robust sea-sky-line detection algorithm under complicated sea-sky background. Opto-Electron Eng 33(8):5–10. https://doi.org/10.3969/j.issn.1003-501X.2006.08.002

    Article  Google Scholar 

  11. Huang Y, Fan N, Jie L (2008) A method of ship position based on seasky-line detection. J Project Rockets Missiles Guid 28(5):286–288

    Google Scholar 

  12. Liu X, Zhao C, Zhang S et al (2017) A 2-layered structure sea-sky-line detection algorithm based on regional optimal variance. In: 2017 13th IEEE International Conference on Electronic Measurement Instruments 486–490. https://doi.org/10.1109/ICEMI.2017.8265991

  13. Prasad DK, Deepu R, Lily R et al (2016) Muscowert: multiscale consistence of weighted edge radon transform for horizon detection in maritime images. J Opt Soc Am A Opt Image Sci Vis 33(12):2491–2500. https://doi.org/10.1364/JOSAA.33.002491

    Article  Google Scholar 

  14. Schwendeman M, Thomson J (2015) A horizon-tracking method for shipboard video stabilization and rectification. J Atmos Ocean Technol 32(1):164–176. https://doi.org/10.1175/JTECH-D-14-00047.1

    Article  Google Scholar 

  15. Liang D, Zhang W, Huang Q et al (2015) Robust sea-sky-line detection for complex sea background. In: 2015 IEEE International Conference on Progress in Informatics and Computing (PIC) 317–321. https://doi.org/10.1109/PIC.2015.7489861

  16. Liang D, Liang Y (2020) Horizon detection from electro-optical sensors under maritime environment. IEEE Trans Instrum Meas 69(1):45–53. https://doi.org/10.1109/TIM.2019.2893008

    Article  MathSciNet  Google Scholar 

  17. Gershikov E, Libe T, Kosolapov S (2013) Horizon line detection in marine images: Which method to choose? Int J Intell Syst 6(1):79–88

    Google Scholar 

  18. Sun Y, Fu L (2018) Coarse-fine-stitched: a robust maritime horizon line detection method for unmanned surface vehicle applications. Sensors 18(9):1–18. https://doi.org/10.3390/s18092825

    Article  Google Scholar 

  19. Dumble SJ, Gibbens PW (2012) Horizon profile detection for attitude determination. J Intell Robot Syst 68(3):339–357. https://doi.org/10.1007/s10846-012-9684-7

    Article  Google Scholar 

  20. Fefilatyev S, Smarodzinava V, Hall LO et al (2006) Horizon detection using machine learning techniques. In: 2006 5th International Conference on Machine Learning and Applications (ICMLA’06) 17–21. https://doi.org/10.1109/ICMLA.2006.25

  21. Ahmad T, Bebis G, Nicolescu M et al (2015) An edge-less approach to horizon line detection. In: 2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA) 1095–1102. https://doi.org/10.1109/ICMLA.2015.67

  22. Boroujeni NS, Etemad SA, Whitehead A (2012) Robust horizon detection using segmentation for uav applications. In: 2012 Ninth Conference on Computer and Robot Vision 346–352. https://doi.org/10.1109/CRV.2012.52

  23. Prasad DK, Prasath CK, Rajan D et al (2016) Challenges in video based object detection in maritime scenario using computer vision. Computer Research Repository In arXiv:1606.01079.  https://doi.org/10.48550/arXiv.1608.01079

  24. Hashmani MA, Umair M, Hussain Rizvi SS et al (2020) A survey on edge detection based recent marine horizon line detection methods and their applications. In: 2020 3rd International Conference on Computing, Mathematics and Engineering Technologies (iCoMET) pp 1–5. https://doi.org/10.1109/iCoMET48670.2020.9073895

  25. Kristan M, SulićKenk V, Kovačič S, Perš J (2016) Fast image-based obstacle detection from unmanned surface vehicles. IEEE Trans Cybern 46(3):641–654. https://doi.org/10.1109/TCYB.2015.2412251

    Article  Google Scholar 

  26. Song H, Ren H, Song Y et al (2021) A sea-sky line detection method based on the ransac algorithm in the background of infrared sea-land-sky images. J Russ Laser Res 42:318–327. https://doi.org/10.1007/s10946-021-09965-2

    Article  Google Scholar 

  27. Ettinger S, Nechyba M, Ifju P et al (2002) Vision-guided flight stability and control for micro air vehicles. In: IEEE/RSJ International Conference on Intelligent Robots and Systems 3, 2134–2140. https://doi.org/10.1109/IRDS.2002.1041582

  28. Bai Y, Lei S, Liu L (2021) The ship target detection based on sea-sky-line. In: 2021 6th International Conference on Automation, Control and Robotics Engineering (CACRE) 456–460. https://doi.org/10.1109/CACRE52464.2021.9501336

  29. Ng HF (2006) Automatic thresholding for defect detection. Pattern Recogn Lett 27(14):1644–1649. https://doi.org/10.1016/J.PATREC.2006.03.009

    Article  Google Scholar 

  30. Soille P (2013) Morphological image analysis-principles and applications. Springer Science & Business Media 183–208. https://doi.org/10.1007/978-3-662-03939-7

  31. Guiming S, Jidong S (2016) Remote sensing image edge-detection based on improved canny operator. In: 2016 8th IEEE International Conference on Communication Software and Networks (ICCSN) 652–656. https://doi.org/10.1109/ICCSN.2016.7586604

  32. Truong MTN, Kim S (2018) Automatic image thresholding using otsu’s method and entropy weighting scheme for surface defect detection. Soft Comput 22(2):4197–4203. https://doi.org/10.1007/s00500-017-2709-1

    Article  Google Scholar 

  33. Prasad DK, Rajan D, Rachmawati L et al (2017) Video processing from electro-optical sensors for object detection and tracking in a maritime environment: A survey. IEEE Trans Intell Transp Syst 18(8):1993–2016. https://doi.org/10.1109/TITS.2016.2634580

    Article  Google Scholar 

  34. Mo W, Pei J (2022) Sea-sky line detection in the infrared image based on the vertical grayscale distribution feature. Vis Comput. https://doi.org/10.1007/s00371-022-02455-9

    Article  Google Scholar 

  35. Zardoua Y, Astito A, Boulaala M (2021) A survey on horizon detection algorithms for maritime video surveillance: advances and future techniques. Vis Comput. https://doi.org/10.1007/s00371-021-02321-0

    Article  Google Scholar 

  36. Otsu N (1979) A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybern 9(1):62–66. https://doi.org/10.1109/TSMC.1979.4310076

    Article  Google Scholar 

  37. Canny J (1986) A computational approach to edge detection. IEEE Trans Pattern Anal Mach Intell 6:679–698. https://doi.org/10.1109/TPAMI.1986.4767851

    Article  Google Scholar 

  38. Wei M, Yan Q, Luo F et al (2019) Joint bilateral propagation upsampling for unstructured multi-view stereo. Vis Comput 35:797–809. https://doi.org/10.1007/s00371-019-01688-5

    Article  Google Scholar 

  39. Dai Y, Liu B, Li L et al (2018) Sea-sky-line detection based on local Otsu segmentation and Hough transform. Opto-Electron Eng 45(7):57–65. https://doi.org/10.12086/oee.2018.180039

    Article  Google Scholar 

  40. Haralick R (1979) Statistical and structural approaches to texture. Proc IEEE 67(5):786–804. https://doi.org/10.1109/PROC.1979.11328

    Article  Google Scholar 

  41. Ganbold U, Akashi T (2020) The real-time reliable detection of the horizon line on high-resolution maritime images for unmanned surface-vehicle. 2020 International Conference on Cyberworlds (CW) 204–210. https://doi.org/10.1109/CW49994.2020.9247845

  42. Guo Y, Wang Q, Qiang Y et al (2022) A real-time horizon detection method based on confidence map in maritime scenarios. 2022 41st Chinese Control Conference (CCC) 6321–6326. https://doi.org/10.23919/CCC55666.2022.9902568

  43. Zhang Y, Wang H, Yao R (2021) A fast sea-sky line detection method based on edlines. 2021 2nd International Conference on Electronics, Communications and Information Technology (CECIT) 48–52. https://doi.org/10.1109/CECIT53797.2021.00016

  44. Shi B, Wang C, Di Y et al (2023) Research on a horizon line detection method for unmanned surface vehicles in complex environments. J Mar Sci Eng 11(6):1130–1144. https://doi.org/10.3390/jmse11061130

    Article  Google Scholar 

  45. Xin Z, Kong S, Wu Y, Zhan G, Yu J (2022) A hierarchical stabilization control method for a three-axis gimbal based on sea-sky-line detection. Sensors 22(7):2587–2600. https://doi.org/10.3390/s22072587

    Article  Google Scholar 

  46. Zhang P, Xi J, Zhu L (2021) Improved Canny edge detection based on Otsu algorithm of gradient extremum. J Shenyang Inst Aeronaut Eng 38(5):58–65. https://doi.org/10.3969/j.issn.2095-1248.2021.05.009

    Article  Google Scholar 

Download references

Acknowledgements

The authors would like to thank the anonymous reviewers for their valuable comments. And the authors would like to thank Kristan et al. for providing the data sets.

Funding

This work was supported in part by the national key research and development plan (No. 2021YFB3901501, No. 2021YFB3901502, No. 2021YFC280100), science and technology plan of liaoning province (No. 2021JH1/10400008) and Cultivation Program for the Excellent Doctoral Dissertation of Dalian Maritime University.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Qing Hu.

Ethics declarations

Conflict of interest

We 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

Zhang, Y., Hu, Q., Li, D. et al. Texture feature-based local adaptive Otsu segmentation and Hough transform for sea-sky line detection. Multimed Tools Appl 83, 34477–34498 (2024). https://doi.org/10.1007/s11042-023-17012-2

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-023-17012-2

Keywords

Navigation