Skip to main content

Advertisement

Springer Nature Link
Log in

Camouflaged object detection via boundary refinement

  • Regular Paper
  • Published:
Multimedia Systems Aims and scope Submit manuscript

Abstract

In camouflaged object detection (COD), wholly and accurately segmenting the foreground from the background is a major focus of research. However, the similarity in color and texture between foreground targets and the background makes it difficult to distinguish them. Despite numerous deep learning networks utilizing different approaches for camouflaged object detection, precisely identifying object contours and enhancing the model’s resistance to interference from similar backgrounds remains a challenging problem. To address this issue, this paper proposes a camouflaged object detection network named boundary refinement network (BRNet) that achieves a fine-grained description of object contours by utilizing boundary semantic information constraints. Firstly, the multi-level asymmetric convolution module (MACM) is designed to enhance feature representation within the backbone architecture via a sequence of asymmetric convolutions and cross-layer connections. Additionally, the Boundary Constraint Guided Module (BCGM) is proposed to impose constraints on foreground shape and refine constrained foreground contours. Lastly, we introduce the Boundary Fusion Extraction Module (BFEM), which enables interaction between boundaries and objects in an additional dimension, leading to the generation of prediction results. Extensive quantitative and qualitative experiments conducted on three datasets demonstrate that BRNet performs well on the camouflaged object detection task, achieving superior results compared to 21 state-of-the-art approaches.

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
$34.99 /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 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

Similar content being viewed by others

Data availability

The original dataset and the results generated through our network can be requested from the corresponding authors via email within a reasonable range of requirements. No datasets were generated or analysed during the current study.

References

  1. Cuthill, I.C., Stevens, M., Sheppard, J., et al.: Disruptive coloration and background pattern matching. Nature 434, 72–74 (2005). https://doi.org/10.1038/nature03312

    Article  MATH  Google Scholar 

  2. Liu, L., Ouyang, W., Wang, X., et al.: Deep learning for generic object detection: a survey. Int. J. Comput. Vis. 128, 261–318 (2020). https://doi.org/10.1007/s11263-019-01247-4

    Article  MATH  Google Scholar 

  3. Li G, Zhu C (2017) A Three-Pathway Psychobiological Framework of Salient Object Detection Using Stereoscopic Technology. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW). pp 3008–3014

  4. Owens A, Barnes C, Flint A, et al (2014) Camouflaging an object from many viewpoints. In: 2014 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp 2782–2789

  5. Troscianko, T., Benton, C.P., Lovell, P.G., et al.: Camouflage and visual perception. Philos Trans R Soc B Biol Sci 364, 449–461 (2009). https://doi.org/10.1098/rstb.2008.0218

    Article  MATH  Google Scholar 

  6. Fan DP, Ji GP, Sun G, et al (2020) Camouflaged object detection. In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp 2774–2784

  7. Lv Y, Zhang J, Dai Y, et al (2021) Simultaneously Localize, Segment and Rank the Camouflaged Objects. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

  8. Mei H, Ji GP, Wei Z, et al (2021) Camouflaged Object Segmentation with Distraction Mining. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp 8768–8777

  9. Ji, G.P., Fan, D.P., Chou, Y.C., et al.: Deep gradient learning for efficient camouflaged object detection. Mach Intell Res 20, 92–108 (2023). https://doi.org/10.1007/s11633-022-1365-9

    Article  MATH  Google Scholar 

  10. Sun Y, Wang S, Chen C, et al (2022) Boundary-Guided Camouflaged Object Detection. In: 2022 International Joint Conference on Artificial Intelligence(IJCAI). pp 1335–1341

  11. Bi, H., Tong, Y., Zhang, J., et al.: Depth alignment interaction network for camouflaged object detection. Multimed Syst 30, 51 (2024). https://doi.org/10.1007/s00530-023-01250-3

    Article  MATH  Google Scholar 

  12. Fan, D.-P., Ji, G.-P., Xu, P., et al.: Advances in deep concealed scene understanding. Vis Intell 1, 16 (2023). https://doi.org/10.1007/s44267-023-00019-6

    Article  MATH  Google Scholar 

  13. He, J., Zhang, Y., Chung, M., et al.: Whole-body tumor segmentation from PET/CT images using a two-stage cascaded neural network with camouflaged object detection mechanisms. Med. Phys. 50, 6151–6162 (2023). https://doi.org/10.1002/mp.16438

    Article  MATH  Google Scholar 

  14. Bhajantri NU, Nagabhushan P (2006) Camouflage defect identification: A novel approach. In: 2006 International Conference on Information Technology (ICIT). pp 145–148

  15. Boot, W.R., Neider, M.B., Kramer, A.F.: Training and transfer of training in the search for camouflaged targets. Atten. Percept. Psychophys. 71, 950–963 (2009). https://doi.org/10.3758/app.71.4.950

    Article  MATH  Google Scholar 

  16. Fan, D.P., Ji, G.P., Cheng, M.M., et al.: Concealed object detection. IEEE Trans. Pattern Anal. Mach. Intell. 44, 6024–6042 (2022). https://doi.org/10.1109/TPAMI.2021.3085766

    Article  MATH  Google Scholar 

  17. Yin, B., Zhang, X., Hou, Q., et al.: CamoFormer: masked separable attention for camouflaged object detection. IEEE Trans Pattern Anal Mach Intell (2024). https://doi.org/10.1109/TPAMI.2024.3438565

    Article  MATH  Google Scholar 

  18. He R, Dong Q, Lin J, et al (2023) Weakly-supervised Camouflaged object detection with scribble annotations. In: 2023 Proceedings of the AAAI Conference on Artificial Intelligence(AAAI). pp 781–789

  19. Pang Y, Zhao X, Xiang TZ, et al (2022) Zoom in and out: a mixed-scale triplet network for Camouflaged object detection. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp 2150–2160

  20. Zhu C, Yan W, Liu S, et al (2019) Salient contour-aware based twice learning strategy for saliency detection. In: 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW). pp 2541–2548

  21. Yu, J., Chen, S., Lu, L., et al.: Alternate guidance network for boundary-aware camouflaged object detection. Mach. Vis. Appl. 34, 69 (2023). https://doi.org/10.1007/s00138-023-01424-z

    Article  MATH  Google Scholar 

  22. Liu, K., Qiu, T., Yu, Y., et al.: Edge-guided camouflaged object detection via multi-level feature integration. Sensors 23, 5789 (2023). https://doi.org/10.3390/s23135789

    Article  MATH  Google Scholar 

  23. Li, P., Yan, X., Zhu, H., et al.: FindNet: can you find me? Boundary-and-texture enhancement network for Camouflaged object detection. IEEE Trans. Image Process. 31, 6396–6411 (2022). https://doi.org/10.1109/TIP.2022.3189828

    Article  MATH  Google Scholar 

  24. Wang, W., Xie, E., Li, X., et al.: PVT v2: improved baselines with pyramid vision transformer. Comput Vis Media 8, 415–424 (2022). https://doi.org/10.1007/s41095-022-0274-8

    Article  MATH  Google Scholar 

  25. DIng X, Guo Y, DIng G, et al (2019) ACNet: Strengthening the kernel skeletons for powerful CNN via asymmetric convolution blocks. In: 2019 IEEE/CVF International Conference on Computer Vision (ICCV). pp 1911–1920

  26. Xu, X., Zhu, M., Yu, J., et al.: Boundary guidance network for camouflage object detection. Image Vis. Comput. 114, 104283 (2021). https://doi.org/10.1016/j.imavis.2021.104283

    Article  MATH  Google Scholar 

  27. Xiao, J., Chen, T., Hu, X., et al.: Boundary-guided context-aware network for camouflaged object detection. Neural Comput. Appl. 35, 15075–15093 (2023). https://doi.org/10.1007/s00521-023-08502-3

    Article  MATH  Google Scholar 

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

  29. Kingma DP, Ba JL (2015) Adam: a method for stochastic optimization. In: 2015 International Conference on Learning Representations(ICLR). p 14

  30. Le, T.N., Nguyen, T.V., Nie, Z., et al.: Anabranch network for camouflaged object segmentation. Comput. Vis. Image Underst. 184, 45–56 (2019). https://doi.org/10.1016/j.cviu.2019.04.006

    Article  MATH  Google Scholar 

  31. Sun Y, Chen G, Zhou T, et al (2021) Context-aware Cross-level Fusion Network for Camouflaged Object Detection. In: 2021 International Joint Conference on Artificial Intelligence(IJCAI). pp 1025–1031

  32. Wang, K., Bi, H., Zhang, Y., et al.: DC-Net: a dual-branch, dual-guidance and cross-refine network for camouflaged object detection. IEEE Trans. Ind. Electron. 69, 5364–5374 (2022). https://doi.org/10.1109/TIE.2021.3078379

    Article  MATH  Google Scholar 

  33. Zhang, C., Wang, K., Bi, H., et al.: Camouflaged object detection via neighbor connection and hierarchical information transfer. Comput. Vis. Image Underst. 221, 103450 (2022). https://doi.org/10.1016/j.cviu.2022.103450

    Article  Google Scholar 

  34. Zhai Q, Li X, Yang F, et al (2021) Mutual graph learning for camouflaged object detection. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp 12992–13002

  35. Ji, G.P., Zhu, L., Zhuge, M., et al.: Fast camouflaged object detection via edge-based reversible re-calibration network. Pattern Recognit 123, 108414 (2022). https://doi.org/10.1016/j.patcog.2021.108414

    Article  Google Scholar 

  36. Zhang M, Xu S, Piao Y, et al (2022) PreyNet: Preying on Camouflaged Objects. In: 2022 ACM International Conference on Multimedia(ACM MM). pp 5323–5332

  37. Wu Z, Paudel DP, Fan DP, et al (2023) Source-free Depth for Object Pop-out. In: 2023 IEEE/CVF International Conference on Computer Vision (ICCV). pp 1032–1042

  38. Mei, H., Yang, X., Zhou, Y., et al.: Distraction-aware camouflaged object segmentation. Sci Sin Inf 54, 653–673 (2024). https://doi.org/10.1360/SSI-2022-0138

    Article  Google Scholar 

  39. Liu Z, Zhang Z, Tan Y, et al (2022) Boosting Camouflaged object detection with dual-task interactive transformer. In: 2022 International Conference on Pattern Recognition (ICPR). pp 140–146

  40. Hu X, Wang S, Qin X, et al (2023) High-resolution iterative feedback network for camouflaged object detection. In: 2023 Proceedings of the AAAI Conference on Artificial Intelligence(AAAI). pp 881–889

  41. Pang, Y., Zhao, X., Xiang, T.Z., et al.: ZoomNeXt: a unified collaborative pyramid network for camouflaged object detection. IEEE Trans Pattern Anal Mach Intell (2024). https://doi.org/10.1109/TPAMI.2024.3417329

    Article  MATH  Google Scholar 

  42. Jha D, Smedsrud PH, Riegler MA, et al (2020) Kvasir-SEG: a segmented polyp dataset. In: 2020 Multimedia Modeling(MMM). pp 451–462

  43. Bernal, J., Sánchez, F.J., Fernández-Esparrach, G., et al.: WM-DOVA maps for accurate polyp highlighting in colonoscopy: validation vs. saliency maps from physicians. Comput. Med. Imaging Graph. 43, 99–111 (2015). https://doi.org/10.1016/j.compmedimag.2015.02.007

    Article  MATH  Google Scholar 

  44. Silva, J., Histace, A., Romain, O., et al.: Toward embedded detection of polyps in WCE images for early diagnosis of colorectal cancer. Int. J. Comput. Assist. Radiol. Surg. 9, 283–293 (2014). https://doi.org/10.1007/s11548-013-0926-3

    Article  Google Scholar 

  45. Mamonov, A.V., Figueiredo, I.N., Figueiredo, P.N., et al.: Automated polyp detection in colon capsule endoscopy. IEEE Trans. Med. Imaging 33, 1488–1502 (2014). https://doi.org/10.1109/TMI.2014.2314959

    Article  MATH  Google Scholar 

  46. Vázquez, D., Bernal, J., Javier Sánchez, F., et al.: A benchmark for endoluminal scene segmentation of colonoscopy images. J Healthc Eng 2017, 4037190 (2017). https://doi.org/10.1155/2017/4037190

    Article  MATH  Google Scholar 

  47. Fan D-P, Ji G-P, Zhou T, et al (2020) PraNet: parallel reverse attention network for polyp segmentation. In: 2020 Medical Image Computing and Computer Assisted Intervention(MICCAI). pp 263–273

  48. Trinh Q-H, Nguyen M-V, Thi P-T (2024) KDAS: knowledge distillation via attention supervision framework for polyp segmentation. arXiv-Preprint. https://doi.org/10.48550/arXiv.2312.08555

  49. Ronneberger O, Fischer P, Brox T (2015) U-net: convolutional networks for biomedical image segmentation. In: 2015 Medical Image Computing and Computer Assisted Intervention(MICCAI). pp 234–241

  50. Fang Y, Chen C, Yuan Y, et al (2019) Selective feature aggregation network with area-boundary constraints for polyp segmentation. In: 2019 Medical Image Computing and Computer Assisted Intervention(MICCAI). pp 302–310

Download references

Acknowledgements

This work is supported by the National Natural Science Foundation of China (62002100, 62202142).

Funding

National Natural Science Foundation of China, 62002100, 62202142.

Author information

Authors and Affiliations

  1. School of Artificial Intelligence, Henan University, Zhengzhou, 450046, China

    Miaohui Zhang, Chenxing Shen & Yangyang Deng

  2. Eurasia International School of Henan University, Kaifeng, 475001, China

    Li Wang

Authors
  1. Miaohui Zhang
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Chenxing Shen
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Yangyang Deng
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Li Wang
    View author publications

    You can also search for this author inPubMed Google Scholar

Contributions

Miaohui Zhang: supervision, work coordination. Chenxing Shen: methodology, software. Yangyang Deng: data support, resources. LI Wang: editing, supervision.

Corresponding author

Correspondence to Li Wang.

Ethics declarations

Conflict of 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. The authors declare no competing interests.

Additional information

Communicated by Junyu Gao.

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, M., Shen, C., Deng, Y. et al. Camouflaged object detection via boundary refinement. Multimedia Systems 31, 56 (2025). https://doi.org/10.1007/s00530-024-01662-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s00530-024-01662-9

Keywords