Skip to main content
SpringerLink
Log in

Surface Target Saliency Detection in Complex Environments

  • Conference paper
  • First Online:
Advanced Intelligent Computing Technology and Applications (ICIC 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14087))

Included in the following conference series:

  • 904 Accesses

Abstract

Water surface images are susceptible to interference from complex environments, resulting in low contrast of the acquired images and increased noise interference, which seriously affects the accuracy of extracting saliency regions. To address the above problems, a pyramidal feature fusion network for water surface target saliency detection method is proposed. First, a perceptual field enhancement module is designed to enrich the initial feature information extracted from the backbone network; subsequently, the adjacent features outputted in the previous step are fused layer by layer in a pyramidal manner by improving the cross-feature module; finally, the model is supervised and trained with pixel location-aware loss to output the water surface target saliency map. The experimental results show that the comprehensive performance of the method proposed in this paper is superior, and the S-measure, E-measure and F-measure metrics are improved by 1.3%, 2.3% and 2.6%, respectively, and the MAE is reduced by 16.7% compared with the F3Net method.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 99.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 129.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Jiang, F., Kong, B., Qian, J., Wang, C., Yang, J.: Review on salient object detection. Meas. Control Technol. (2021)

    Google Scholar 

  2. Itti, L., Koch, C., Niebur, E.: A model of saliency-based visual attention for rapid scene analysis. IEEE Trans. Pattern Anal. Mach. Intell. 20(11), 1254–1259 (1998)

    Article  Google Scholar 

  3. Liu, T., et al.: Learning to detect a salient object. IEEE Trans. Pattern Anal. Mach. Intell. 33(2), 353–367 (2010)

    MathSciNet  Google Scholar 

  4. Hou, X., Zhang, L.: Saliency detection: a spectral residual approach. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8. IEEE (2007)

    Google Scholar 

  5. Liu, J.J., Hou, Q., Cheng, M.M., Feng, J., Jiang, J.: A simple pooling-based design for real-time salient object detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3917–3926 (2019)

    Google Scholar 

  6. Qin, X., Zhang, Z., Huang, C., Gao, C., Dehghan, M., Jagersand, M.: BASNet: boundary-aware salient object detection. In: Proceedings of the IEEE/CVF conference on Computer Vision and Pattern Recognition, pp. 7479–7489 (2019)

    Google Scholar 

  7. Wu, J., Sun, F., Xu, R., Meng, J., Wang, F.: Aggregate interactive learning for RGB-D salient object detection. Expert Syst. Appl. 195, 116614 (2022)

    Article  Google Scholar 

  8. Xie, C., Xia, C., Ma, M., Zhao, Z., Chen, X., Li, J.: Pyramid grafting network for one-stage high resolution saliency detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11717–11726 (2022)

    Google Scholar 

  9. He, Y., Liu, K.: Detection of sea-surface saliency object based on convolutional neural network. Comput. Eng. Appl. 57(6), 9 (2021)

    Google Scholar 

  10. Gu, X., Zhu, L., Liu, H., Li, G., Bu, W., Liu, G.: Detection of surface garbage significance based on spatial temporal information fusion. Electron. Meas. Technol. 45(11), 7 (2022)

    Google Scholar 

  11. Zhang, X., Zhang, T., Liu, Z., Jiang, T.: Infrared salient object detection of sea background based on lightweight CNN. J. Shandong Univ. (Eng. Sci.) (002), 052 (2022)

    Google Scholar 

  12. Yang, C., Zhang, L., Lu, H., Ruan, X., Yang, M.H.: Saliency detection via graph-based manifold ranking. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3166–3173 (2013)

    Google Scholar 

  13. Wang, L., et al.: Learning to detect salient objects with image-level supervision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 136–145 (2017)

    Google Scholar 

  14. Yan, Q., Xu, L., Shi, J., Jia, J.: Hierarchical saliency detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1155–1162 (2013)

    Google Scholar 

  15. Fan, D.P., Gong, C., Cao, Y., Ren, B., Cheng, M.M., Borji, A.: Enhanced-alignment measure for binary foreground map evaluation. arXiv preprint arXiv:1805.10421 (2018)

  16. Movahedi, V., Elder, J.H.: Design and perceptual validation of performance measures for salient object segmentation. In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition-Workshops, pp. 49–56. IEEE (2010)

    Google Scholar 

  17. Li, Y., Hou, X., Koch, C., Rehg, J.M., Yuille, A.L.: The secrets of salient object segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 280–287 (2014)

    Google Scholar 

  18. Li, G., Yu, Y.: Visual saliency detection based on multiscale deep CNN features. IEEE Trans. Image Process. 25(11), 5012–5024 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  19. Wang, W., Lai, Q., Fu, H., Shen, J., Ling, H., Yang, R.: Salient object detection in the deep learning era: an in-depth survey. IEEE Trans. Pattern Anal. Mach. Intell. 44(6), 3239–3259 (2021)

    Article  Google Scholar 

  20. Cheng, M.M., Mitra, N.J., Huang, X., Torr, P.H., Hu, S.M.: Global contrast based salient region detection. IEEE Trans. Pattern Anal. Mach. Intell. 37(3), 569–582 (2014)

    Article  Google Scholar 

  21. Zhai, Y., Shah, M.: Visual attention detection in video sequences using spatiotemporal cues. In: Proceedings of the 14th ACM International Conference on Multimedia, pp. 815–824 (2006)

    Google Scholar 

  22. Achanta, R., Hemami, S., Estrada, F., Susstrunk, S.: Frequency-tuned salient region detection. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1597–1604. IEEE (2009)

    Google Scholar 

  23. Wu, Z., Su, L., Huang, Q.: Cascaded partial decoder for fast and accurate salient object detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3907–3916 (2019)

    Google Scholar 

  24. Qin, X., Zhang, Z., Huang, C., Dehghan, M., Zaiane, O.R., Jagersand, M.: U2-Net: going deeper with nested u-structure for salient object detection. Pattern Recogn. 106, 107404 (2020)

    Article  Google Scholar 

  25. Wei, J., Wang, S., Huang, Q.: F3Net: fusion, feedback and focus for salient object detection. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 12321–12328 (2020)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan, China

    Benxin Yang & Yaojie Chen

  2. Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System, Wuhan University of Science and Technology, Wuhan, China

    Benxin Yang & Yaojie Chen

Authors
  1. Benxin Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yaojie Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yaojie Chen .

Editor information

Editors and Affiliations

  1. Department of Computer Science, Eastern Institute of Technology, Zhejiang, China

    De-Shuang Huang

  2. University of Wollongong, North Wollongong, NSW, Australia

    Prashan Premaratne

  3. Zhengzhou University of Light Industry, Zhengzhou, China

    Baohua Jin

  4. Zhong Yuan University of Technology, Zhengzhou, China

    Boyang Qu

  5. University of Ulsan, Ulsan, Korea (Republic of)

    Kang-Hyun Jo

  6. Department of Computer Science, Liverpool John Moores University, Liverpool, UK

    Abir Hussain

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Yang, B., Chen, Y. (2023). Surface Target Saliency Detection in Complex Environments. In: Huang, DS., Premaratne, P., Jin, B., Qu, B., Jo, KH., Hussain, A. (eds) Advanced Intelligent Computing Technology and Applications. ICIC 2023. Lecture Notes in Computer Science, vol 14087. Springer, Singapore. https://doi.org/10.1007/978-981-99-4742-3_55

Download citation

  • DOI: https://doi.org/10.1007/978-981-99-4742-3_55

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-4741-6

  • Online ISBN: 978-981-99-4742-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation