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Semantic-spatial guided context propagation network for camouflaged object detection

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Abstract

Camouflaged object detection (COD) aims to detect objects that blend in with their surroundings and is a challenging task in computer vision. High-level semantic information and low-level spatial information play important roles in localizing camouflaged objects and reinforcing spatial cues. However, current COD methods directly connect high-level features with low-level features, ignoring the importance of the respective features. In this paper, we design a Semantic-spatial guided Context Propagation Network (SCPNet) to efficiently mine semantic and spatial features while enhancing their feature representations. Firstly, we design a twin positioning module (TPM) to explore semantic cues to accurately locate camouflaged objects. Afterward, we introduce a spatial awareness module (SAM) to mine spatial cues in shallow features deeply. Finally, we develop a context propagation module (CPM) to assign semantic and spatial cues to multi-level features and enhance their feature representations. Experimental results show that our SCPNet outperforms state-of-the-art methods on three challenging datasets. Codes will be made available at https://github.com/RJC0608/SCPNet.

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Data Availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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Acknowledgements

This paper was supported by the Natural Science Foundation of Heilongjiang Province (No.LH2022F005).

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Author notes

  1. Junchao Ren and Qiao Zhang contributed equally to this work.

Authors and Affiliations

  1. School of Electrical Information Engineering, Northeast Petroleum University, Daqing, 163318, China

    Junchao Ren, Yuxi Zhong, Yanliang Ge & Hongbo Bi

  2. School of Computer Science and Technology, China University of Petroleum (East China), Qingdao, 266580, China

    Qiao Zhang

  3. Henan Engineering Laboratory of Intelligent Medical Internet of Things Technology, Pingdingshan University, Pingdingshan, 467000, China

    Bingbing Kang

  4. Daqing Branch of China Mobile Communications Group Heilongjiang Co. LTD, Daqing, 163318, China

    Min He

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  1. Junchao Ren
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  5. Min He
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Contributions

Junchao Ren: Methodology, Writing-Original draft preparation and Software. Qiao Zhang: Data curation, Software, Methodology. Bingbing Kang: Visualization, Investigation. Yuxi Zhong: Conceptualization, Methodology. Min He: Software, Conceptualization. Yanliang Ge: Writing-Reviewing, Editing and Supervision. Hongbo Bi: Supervision, Writing-Reviewing.

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Correspondence to Yanliang Ge or Hongbo Bi.

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Ren, J., Zhang, Q., Kang, B. et al. Semantic-spatial guided context propagation network for camouflaged object detection. Appl Intell 55, 349 (2025). https://doi.org/10.1007/s10489-025-06264-0

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