Skip to main content
SpringerLink
Log in

Dynamic interactive refinement network for camouflaged object detection

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Automatically identifying objects similar to the surroundings is a complex and difficult task in real-world scenarios. In addition to the high intrinsic similarity between camouflaged objects and their backgrounds, these objects are usually diverse in scale and blurred in appearance. And the deceptive nature of the camouflaged objects introduces lots of noise into the features and generates inaccurate segmentation map extracted by deep learning model. We tackle these problems by proposing a novel dynamic interactive refinement network (DIRNet), which aims to make the features exploit effective details and semantics together as well as discard interference information. Specifically, we utilize bilateral interaction module (BIM) to interact with foreground and background information to conduct contextual exploration, which can capture more meaningful details and refine the confusion. Additionally, in the purpose of retaining the appropriate information and erasing noise, we design an adjacent aggregation interaction module (AAIM) to integrate the adjacent multi-level features with attention coefficients for each layer. The final results are obtained through the dynamic refinement of the BIM and AAIM. Extensive quantitative and qualitative experiments on four public benchmark datasets demonstrate that our proposed DIRNet is an effective COD framework and outperforms 14 state-of-the-art models.

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

Access this article

Log in via an institution

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

Similar content being viewed by others

Data availibility

The data used in this paper are publicly available.

References

  1. Fan D-P, Zhou T, Ji G-P, Zhou Y, Chen G, Fu H, Shen J, Shao L (2020) Inf-net: automatic covid-19 lung infection segmentation from CT images. IEEE Trans Med Imaging 39(8):2626–2637

    Article  PubMed  Google Scholar 

  2. Wu Y-H, Gao S-H, Mei J, Xu J, Fan D-P, Zhang R-G, Cheng M-M (2021) Jcs: an explainable covid-19 diagnosis system by joint classification and segmentation. IEEE Trans Image Process 30:3113–3126

    Article  ADS  PubMed  Google Scholar 

  3. Xie E, Wang W, Wang W, Ding M, Shen C, Luo P (2020) Segmenting transparent objects in the wild, In: Computer vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XIII 16. Springer, pp. 696–711

  4. Fan D-P, Ji G-P, Zhou T, Chen G, Fu H, Shen J, Shao L (2020) Pranet: parallel reverse attention network for polyp segmentation, In: International conference on medical image computing and computer-assisted intervention. Springer, pp. 263–273

  5. Nogueira-Rodriguez A, Dominguez-Carbajales R, Campos-Tato F, Herrero J, Puga M, Remedios D, Rivas L, Sanchez E, Iglesias A, Cubiella J, Fdez-Riverola F (2022) Real-time polyp detection model using convolutional neural networks. Neural Comput Appl 34(13):10375–10396

    Article  Google Scholar 

  6. Yu S, Zhang B, Xiao J, Lim EG (2021) Structure-consistent weakly supervised salient object detection with local saliency coherence, In: Proceedings of the AAAI conference on artificial intelligence (AAAI)

  7. Kompella A, Kulkarni RV (2021) A semi-supervised recurrent neural network for video salient object detection. Neural Comput Appl 33:2065–2083

    Article  Google Scholar 

  8. Ma M, Xia C, Li J (2021) Pyramidal feature shrinking for salient object detection. Proceed AAAI Conf Artif Intell 35(3):2311–2318

    Google Scholar 

  9. Wang Z, Zhang Y, Liu Y, Wang Z, Coleman S, Kerr D (2022) Tf-sod: a novel transformer framework for salient object detection. Neural Comput Appl 34:11789–11806

    Article  Google Scholar 

  10. Xu B, Liang H, Liang R, Chen P (2021) Locate globally, segment locally: a progressive architecture with knowledge review network for salient object detection. Proceed AAAI Conf Artif Intell 35(4):3004–3012

    Google Scholar 

  11. Chen T, Hu X, Xiao J, Zhang G, Wang S (2022) Cfidnet: cascaded feature interaction decoder for rgb-d salient object detection’’. Neural Comput Appl 34:7547–7563

    Article  Google Scholar 

  12. Fan D-P, Ji G-P, Sun G, Cheng M-M, Shen J, Shao L (2020) Camouflaged object detection, In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2777–2787

  13. Yan J, Le T-N, Nguyen K-D, Tran M-T, Do T-T, Nguyen TV (2021) Mirrornet: bio-inspired camouflaged object segmentation. IEEE Access 9:43290–43300

    Article  Google Scholar 

  14. Ren J, Hu X, Zhu L, Xu X, Xu Y, Wang W, Deng Z, Heng P-A (2021) Deep texture-aware features for camouflaged object detection, arXiv preprint arXiv:2102.02996

  15. Zhai Q, Li X, Yang F, Chen C, Cheng H, Fan D-P (2021) Mutual graph learning for camouflaged object detection, In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 12997–13007

  16. Chen S, Wang B, Tan X, Xuelong H (2020) Embedding attention and residual network for accurate salient object detection. IEEE Trans Cybernet 50:2050–2062

    Article  Google Scholar 

  17. Li A, Zhang J, Lv Y, Liu B, Zhang T, Dai Y (2021) Uncertainty-aware joint salient object and camouflaged object detection, In: Proceedings of the 2021 IEEE/CVF conference on computer vision and pattern recognition, pp. 10071–10081

  18. Wang K, Bi H, Zhang Y, Zhang C, Liu Z, Zheng S (2021) D 2 c-net: a dual-branch, dual-guidance and cross-refine network for camouflaged object detection. IEEE Trans Ind Electr 69(5):5364–5374

    Article  Google Scholar 

  19. Dong B, Zhuge M, Wang Y, Bi H, Chen G (2021) Toward accurate camouflaged object detection with mixture convolution and interactive fusion, arXiv preprint arXiv:2101.05687

  20. Mei H, Ji G-P, Wei Z, Yang X, Wei X, Fan D-P (2021) Camouflaged object segmentation with distraction mining, In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 8772–8781

  21. Fan D-P, Ji G-P, Cheng M-M, Shao L (2021) Concealed object detection, In: IEEE transactions on pattern analysis and machine intelligence, pp. 1–1

  22. Mondal A (2020) Camouflaged object detection and tracking: a survey. Int J Image Graphics 20(04):2050028

    Article  Google Scholar 

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

    Article  Google Scholar 

  24. Bhajantri NU, Nagabhushan P (2006) Camouflage defect identification: a novel approach, In: 9th International conference on information technology (ICIT’06). IEEE, pp. 145–148

  25. 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 

  26. Siricharoen P, Aramvith S, Chalidabhongse T, Siddhichai S (2010) Robust outdoor human segmentation based on color-based statistical approach and edge combination, In: The 2010 international conference on green circuits and systems. IEEE, pp. 463–468

  27. Skurowski P, Abdulameer H, Błaszczyk J, Depta T, Kornacki A, Kozieł P (2018) Animal camouflage analysis: chameleon database, Unpublished Manuscript, 2(6):7

  28. Le T-N, Nguyen TV, Nie Z, Tran M-T, Sugimoto A (2019) Anabranch network for camouflaged object segmentation. Comput Vis Image Underst 184:45–56

    Article  Google Scholar 

  29. Zhu J, Zhang X, Zhang S, Liu J (2021) Inferring camouflaged objects by texture-aware interactive guidance network. Proceed AAAI Conf Artif Intell 35(4):3599–3607

    Google Scholar 

  30. Li A, Zhang J, Lv Y, Liu B, Zhang T, Dai Y (2021) Uncertainty-aware joint salient object and camouflaged object detection, In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 10071–10081

  31. Chen S, Tan X, Wang B, Hu X (2018) Reverse attention for salient object detection, In: Proceedings of the European conference on computer vision (ECCV), pp. 234–250

  32. Ronneberger O, Fischer P, Brox T (2015) U-net: convolutional networks for biomedical image segmentation, In: International conference on medical image computing and computer-assisted intervention. Springer, pp. 234–241

  33. Zhang P, Wang D, Lu H, Wang H, Ruan X (2017) Amulet: aggregating multi-level convolutional features for salient object detection, In: Proceedings of the IEEE international conference on computer vision, pp. 202–211

  34. Wei J, Wang S, Huang Q (2020) F\(^3\)net: fusion, feedback and focus for salient object detection. Proceed AAAI Conf Art Intell 34(7):12321–12328

    Google Scholar 

  35. Gao S, Cheng M-M, Zhao K, Zhang X-Y, Yang M-H, Torr PH (2019) Res2net: a new multi-scale backbone architecture. IEEE Trans Patt Anal Mach Intell 43(2):652–662

    Article  Google Scholar 

  36. Wu Z, Su L, Huang Q (2019) 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

  37. Chen S, Fu Y (2020) Progressively guided alternate refinement network for rgb-d salient object detection, In: European conference on computer vision. Springer, pp. 520–538

  38. Wang T, Zheng Z, Yan C, Zhang J, Sun Y, Zheng B, Yang Y (2021) Each part matters: local patterns facilitate cross-view geo-localization. IEEE Trans Circu Syst Video Technol. https://doi.org/10.1109/TCSVT.2021.3061265

    Article  Google Scholar 

  39. Pang Y, Zhao X, Zhang L, Lu H (2020) Multi-scale interactive network for salient object detection, In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 9413–9422

  40. Qin X, Zhang Z, Huang C, Gao C, Dehghan M, Jagersand M (2019) Basnet: boundary-aware salient object detection. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 7479–7489

  41. Luo Z, Mishra A, Achkar A, Eichel J, Li S, Jodoin P-M (2017) Non-local deep features for salient object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 6609–6617

  42. Wu Z, Su L, Huang Q (2019) Stacked cross refinement network for edge-aware salient object detection. In: Proceedings of the IEEE/CVF international conference on computer vision, pp. 7264–7273

  43. Zhao J-X, Liu J-J, Fan D-P, Cao Y, Yang J, Cheng M-M (2019) Egnet: edge guidance network for salient object detection. In: Proceedings of the IEEE/CVF international conference on computer vision, pp. 8779–8788

  44. Zhou H, Xie X, Lai J-H, Chen Z, Yang L (2020) Interactive two-stream decoder for accurate and fast saliency detection. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 9141–9150

  45. Zhang J, Fan D-P, Dai Y, Anwar S, Saleh F, Aliakbarian S, Barnes N (2021) Uncertainty inspired rgb-d saliency detection. IEEE Trans Patt Anal Mach Intell 44(9):5761–5779

    Google Scholar 

  46. Sun Y, Chen G, Zhou T, Zhang Y, Liu N (2021) Context-aware cross-level fusion network for camouflaged object detection, arXiv preprint arXiv:2105.12555

  47. Lv Y, Zhang J, Dai Y, Li A, Liu B, Barnes N, Fan D-P (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

  48. Perazzi F, Krähenbühl P, Pritch Y, Hornung A 2012 Saliency filters: contrast based filtering for salient region detection. In: 2012 IEEE Conference on computer vision and pattern recognition. IEEE, pp. 733–740

  49. 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

  50. Fan D-P, Cheng M-M, Liu Y, Li T, Borji A (2017) Structure-measure: a new way to evaluate foreground maps. In: Proceedings of the IEEE international conference on computer vision, pp. 4548–4557

  51. Fan D-P, Ji G-P, Qin X, Cheng M-M (2021) Cognitive vision inspired object segmentation metric and loss function, Scientia Sinica Informat, 6(6):

  52. Paszke A, Gross S, Massa F, Lerer A, Bradbury J, Chanan G, Killeen T, Lin Z, Gimelshein N, Antiga L et al (2019) Pytorch: an imperative style, high-performance deep learning library. Adv Neural Inf Process Syst 32:8026–8037

    Google Scholar 

  53. Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. Adv Neural Inf Process Syst 25:1097–1105

    Google Scholar 

  54. Kingma DP, Jimmy B (2014) Adam: a method for stochastic optimization, arXiv preprint arXiv:1412.6980

Download references

Acknowledgements

This work was supported by National Nature Science Foundation of China (U21B2024, 61931008, 62071415).

Author information

Authors and Affiliations

  1. Hangzhou Dianzi University, Hangzhou, 310018, China

    Yaoqi Sun, Lidong Ma, Peiyao Shou, Hongfa Wen, YuHan Gao, Yixiu Liu, Chenggang Yan & Haibing Yin

  2. Lishui Institute of Hangzhou Dianzi University, Lishui, 323000, China

    Yaoqi Sun & YuHan Gao

Authors
  1. Yaoqi Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Lidong Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Peiyao Shou
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hongfa Wen
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. YuHan Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Yixiu Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Chenggang Yan
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Haibing Yin
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yixiu Liu.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

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

Yaoqi Sun and Lidong Ma are the joint first author of this work.

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

Sun, Y., Ma, L., Shou, P. et al. Dynamic interactive refinement network for camouflaged object detection. Neural Comput & Applic 36, 3433–3446 (2024). https://doi.org/10.1007/s00521-023-09262-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-023-09262-w

Keywords

Search

Navigation