Skip to main content
Springer Nature Link
Log in

Mscnet: Mask stepwise calibration network for camouflaged object detection

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

Abstract

Camouflaged object detection (COD) aims to accurately segment camouflaged objects blending into the environment and is a challenging task. Most existing deep learning-based COD methods do not explicitly enhance the region information of camouflaged objects, nor do they use the region information for mask calibration. To solve this issue, we propose a novel mask stepwise calibration network (MSCNet) for camouflaged object detection, which achieves high-precision detection of camouflaged objects. Specifically, MSCNet consists of a region information enhancement encoder and a mask stepwise calibration decoder. In the encoder, we first utilize a PVT backbone network to extract different levels of features from camouflaged images. Then, we design a region information enhancement module to enhance the region information of camouflaged objects while suppressing the interference of background information by mining, embedding, and aggregating the region information in different levels of features. In the decoder, we first design a coarse mask generation module to generate coarse prediction masks of camouflaged objects by directly cross-fusing different levels of features extracted by the backbone. In addition, we also design a mask calibration module to calibrate coarse prediction masks of camouflaged objects using the region information of different levels of camouflaged objects as a guide. Extensive experimental results on four benchmark datasets show that our method effectively identifies camouflaged objects and surpasses most state-of-the-art COD methods.

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

Similar content being viewed by others

Data availability

No datasets were generated or analyzed during the current study.

References

  1. Chen G, Liu SJ, Sun YJ et al (2022) Camouflaged object detection via context-aware cross-level fusion. IEEE Trans Circuits Syst Video Technol 32(10):6981–6993

    Article  Google Scholar 

  2. Chu HK, Hsu WH, Mitra NJ et al (2010) Camouflage images. ACM Trans Graph 29(4):51–1

    Article  Google Scholar 

  3. Cong R, Sun M, Zhang S, et al (2023) Frequency perception network for camouflaged object detection. In: Proceedings of the 31st ACM International Conference on Multimedia, pp 1179–1189

  4. Dosovitskiy A, Beyer L, Kolesnikov A, et al (2020) An image is worth 16x16 words: transformers for image recognition at scale. arXiv preprint arXiv:2010.11929

  5. Fan DP, Cheng MM, Liu Y, et al (2017) Structure-measure: A new way to evaluate foreground maps. In: Proceedings of the IEEE International Conference on Computer Vision, pp 4548–4557

  6. Fan DP, Ji GP, Sun G, et al (2020a) Camouflaged object detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 2777–2787

  7. Fan DP, Ji GP, Zhou T, et al (2020b) Pranet: Parallel reverse attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 263–273

  8. Fan DP, Ji GP, Cheng MM et al (2021) Concealed object detection. IEEE Trans Pattern Anal Mach Intell 44(10):6024–6042

    Article  Google Scholar 

  9. Fan DP, Ji GP, Qin X et al (2021) Cognitive vision inspired object segmentation metric and loss function. Sci Sin Inf 51:1475–1489

    Article  Google Scholar 

  10. Han K, Xiao A, Wu E et al (2021) Transformer in transformer. Adv Neural Inf Process Syst 34:15908–15919

    Google Scholar 

  11. He C, Li K, Zhang Y, et al (2023) Camouflaged object detection with feature decomposition and edge reconstruction. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 22046–22055

  12. Huang Z, Ben Y, Luo G, et al (2021) Shuffle transformer: rethinking spatial shuffle for vision transformer. arXiv preprint arXiv:2106.03650

  13. Huang Z, Dai H, Xiang TZ, et al (2023) Feature shrinkage pyramid for camouflaged object detection with transformers. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 5557–5566

  14. Ji GP, Xiao G, Chou YC et al (2022) Video polyp segmentation: a deep learning perspective. Mach Intell Res 19(6):531–549

    Article  Google Scholar 

  15. Ji GP, Zhu L, Zhuge M et al (2022) Fast camouflaged object detection via edge-based reversible re-calibration network. Pattern Recogn 123:108414

    Article  Google Scholar 

  16. Jia Q, Yao S, Liu Y, et al (2022) Segment, magnify and reiterate: Detecting camouflaged objects the hard way. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4713–4722

  17. Kavitha C, Rao BP, Govardhan A (2011) An efficient content based image retrieval using color and texture of image sub blocks. Int J Eng Sci Technol (IJEST) 3(2):1060–1068

    Google Scholar 

  18. Lin CJ, Prasetyo YT (2019) A metaheuristic-based approach to optimizing color design for military camouflage using particle swarm optimization. Color Res Appl 44(5):740–748

    Article  Google Scholar 

  19. Liu L, Wang R, Xie C et al (2019) Pestnet: an end-to-end deep learning approach for large-scale multi-class pest detection and classification. IEEE Access 7:45301–45312

    Article  Google Scholar 

  20. Liu Y, Zhang K, Zhao Y et al (2023) Bi-rrnet: Bi-level recurrent refinement network for camouflaged object detection. Pattern Recogn 139:109514

    Article  Google Scholar 

  21. Liu Z, Lin Y, Cao Y, et al (2021) Swin transformer: hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp 10012–10022

  22. Liu Z, Zhang Z, Tan Y, et al (2022) Boosting camouflaged object detection with dual-task interactive transformer. In: 2022 26th International Conference on Pattern Recognition (ICPR), pp 140–146

  23. Luo XJ, Wang S, Wu Z, et al (2023) Camdiff: camouflage image augmentation via diffusion. CAAI Artif Intell Res 2

  24. Lv Y, Zhang J, Dai Y, et al (2021) Simultaneously localize, segment and rank the camouflaged objects. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 11591–11601

  25. Lv Y, Zhang J, Dai Y et al (2023) Towards deeper understanding of camouflaged object detection. IEEE Trans Circuits Syst Video Technol 33:3462–3476

    Article  Google Scholar 

  26. Margolin R, Zelnik-Manor L, Tal A (2014) How to evaluate foreground maps? In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 248–255

  27. Mei H, Ji GP, Wei Z, et al (2021) Camouflaged object segmentation with distraction mining. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 8772–8781

  28. Pang Y, Zhao X, Xiang TZ, et al (2022) Zoom in and out: a mixed-scale triplet network for camouflaged object detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 2160–2170

  29. Pei J, Cheng T, Fan DP, et al (2022) Osformer: One-stage camouflaged instance segmentation with transformers. In: European Conference on Computer Vision, pp 19–37

  30. Perazzi F, Krähenbühl P, Pritch Y, et al (2012) Saliency filters: contrast based filtering for salient region detection. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp 733–740

  31. Ren J, Hu X, Zhu L, et al (2021) Deep texture-aware features for camouflaged object detection. IEEE

  32. Shamim S, Awan MJ, Mohd Zain A et al (2022) Automatic covid-19 lung infection segmentation through modified unet model. J Healthc Eng 1:6566982

    Google Scholar 

  33. Skurowski P, Abdulameer H, Błaszczyk J et al (2018) Animal camouflage analysis: chameleon database. Unpubl Manuscr 2(6):7

    Google Scholar 

  34. Song Z, Kang X, Wei X et al (2023) Fsnet: focus scanning network for camouflaged object detection. IEEE Trans Image Process 32:2267–2278

    Article  Google Scholar 

  35. Sun D, Jiang S, Qi L (2023) Edge-aware mirror network for camouflaged object detection. In: 2023 IEEE International Conference on Multimedia and Expo (ICME), IEEE, pp 2465–2470

  36. Sun Y, Chen G, Zhou T, et al (2021) Context-aware cross-level fusion network for camouflaged object detection. arXiv preprint arXiv:2105.12555

  37. Tankus A, Yeshurun Y (2001) Convexity-based visual camouflage breaking. Comput Vis Image Underst 82(3):208–237

    Article  Google Scholar 

  38. Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. Adv Neural Inf Process Syst 30

  39. Wang W, Xie E, Li X et al (2022) Pvt v2: improved baselines with pyramid vision transformer. Comput Vis Media 8(3):415–424

    Article  Google Scholar 

  40. Wei J, Wang S, Huang Q (2020) F\(^3\)net: fusion, feedback and focus for salient object detection. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12321–12328

  41. Wu J, Liang W, Hao F et al (2023) Mask-and-edge co-guided separable network for camouflaged object detection. IEEE Signal Process Lett 30:748–752

    Article  Google Scholar 

  42. Xing H, Wang Y, Wei X et al (2023) Go closer to see better: camouflaged object detection via object area amplification and figure-ground conversion. IEEE Trans Circuits Syst Video Technol 33:5444–5457

    Article  Google Scholar 

  43. Xu X, Zhu M, Yu J et al (2021) Boundary guidance network for camouflage object detection. Image Vis Comput 114:104283

    Article  Google Scholar 

  44. Zhai Q, Li X, Yang F, et al (2021) Mutual graph learning for camouflaged object detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 12997–13007

  45. Zhang Q, Ge Y, Zhang C et al (2023) Tprnet: camouflaged object detection via transformer-induced progressive refinement network. Vis Comput 39(10):4593–4607

    Article  Google Scholar 

  46. Zhang Q, Sun X, Chen Y et al (2023) Attention-induced semantic and boundary interaction network for camouflaged object detection. Comput Vis Image Underst 233:103719

    Article  Google Scholar 

  47. Zhao W, Xie S, Zhao F et al (2023) Nowhere to disguise: spot camouflaged objects via saliency attribute transfer. IEEE Trans Image Process 32:3108–3120

    Article  Google Scholar 

  48. Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513

  49. Zhou T, Zhou Y, Gong C et al (2022) Feature aggregation and propagation network for camouflaged object detection. IEEE Trans Image Process 31:7036–7047

    Article  Google Scholar 

  50. Zhuge M, Lu X, Guo Y, et al (2022) Cubenet: X-shape connection for camouflaged object detection. Pattern Recognit 127:108644

    Article  Google Scholar 

Download references

Acknowledgements

This work is supported in part by the Science and Technology Development Plan Project of Henan Province, China (No. 222102110135).

Author information

Authors and Affiliations

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

    Haishun Du, Minghao Zhang, Wenzhe Zhang & Kangyi Qiao

  2. International Joint Research Laboratory for Cooperative Vehicular Networks of Henan, Zhengzhou, 450046, China

    Haishun Du & Minghao Zhang

Authors
  1. Haishun Du
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Minghao Zhang
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Wenzhe Zhang
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Kangyi Qiao
    View author publications

    You can also search for this author inPubMed Google Scholar

Contributions

HD wrote the main manuscript text. MZ provided some suggestions for revision of the manuscript. WZ suggested the structure of the manuscript. KQ gave some help to the typesetting of the manuscript.

Corresponding author

Correspondence to Haishun Du.

Ethics declarations

The authors declare no competing interests.

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

Du, H., Zhang, M., Zhang, W. et al. Mscnet: Mask stepwise calibration network for camouflaged object detection. J Supercomput 80, 24718–24737 (2024). https://doi.org/10.1007/s11227-024-06376-3

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-024-06376-3

Keywords