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Double-branch fusion network with a parallel attention selection mechanism for camouflaged object detection

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Abstract

To meet the challenge of camouflaged object detection (COD), which has a high degree of intrinsic similarity between the object and background, this paper proposes a double-branch fusion network (DBFN) with a parallel attention selection mechanism (PASM). In detail, a schismatic receptive field block (SRF) combined with an attention mechanism for low-level information is performed to learn texture features in one branch, and an integration of the SRF, a hybrid attention mechanism (HAM), and a depth feature polymerization module (DFPM) is employed for high-level information to extract detection features in the other branch. Then, both texture features and detection features are input into the PASM to acquire selective expression matrices. Eventually, the final result is obtained after further selective matrix optimization with atrous spatial pyramid pooling (ASPP) and a residual channel attention block (RCAB) being applied serially. Experimental results on three public datasets verify that our method outperforms the state-of-the-art methods in terms of four evaluation metrics, i.e., mean absolute error (MAE), weighted Fβ measure (F ωβ ), structural measure (Sα), and E-measure (Eφ)

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Acknowledgements

This work was partially supported by National Key R&D Program of China (Grant No. 2018YFB18011001), National Natural Science Foundation of China (Grant No. 62025502), Guangdong Introducing Innovative and Entrepreneurial Teams of ‘The Pearl River Talent Recruitment Program’ (Grant No. 2019ZT08X340), and Guangdong Guangxi Joint Science Key Foundation (Grant No. 2021GXNSFDA076001).

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Authors and Affiliations

  1. Advanced Institute of Photonics Technology, School of Information Engineering, Guangdong University of Technology, Guangzhou, 510006, China

    Junjiang Xiang, Qing Pan, Songnian Fu & Yuwen Qin

  2. Guangdong Provincial Key Laboratory of Photonics Information Technology, Guangdong University of Technology, Guangzhou, 510006, China

    Junjiang Xiang, Qing Pan, Songnian Fu & Yuwen Qin

  3. Guangxi Key Laboratory of Multimedia Communications and Network Technology, School of Computer Electronics and Information, Guangxi University, Nanning, 530004, China

    Zhengrong Zhang

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  1. Junjiang Xiang
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  2. Qing Pan
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  3. Zhengrong Zhang
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  4. Songnian Fu
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  5. Yuwen Qin
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Correspondence to Qing Pan.

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Xiang, J., Pan, Q., Zhang, Z. et al. Double-branch fusion network with a parallel attention selection mechanism for camouflaged object detection. Sci. China Inf. Sci. 66, 162403 (2023). https://doi.org/10.1007/s11432-022-3592-8

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  • DOI: https://doi.org/10.1007/s11432-022-3592-8

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