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Hybrid-Recursive-Refinement Network for Camouflaged Object Detection.

Hailong Chen¹, Xinyi Wang¹, Haipeng Jin¹

¹School of Computer Science and Technology, Harbin University of Science and Technology, Harbin 150080, China.

Journal of Imaging

|September 26, 2025

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This study introduces a novel hybrid deep learning model for camouflaged object detection (COD). The new architecture effectively combines Convolutional Neural Networks (CNNs) and Transformers to improve the detection of hidden objects.

Area of Science:

Computer Vision
Artificial Intelligence
Machine Learning

Background:

Camouflaged object detection (COD) is challenging due to subtle textures and semantic ambiguity.
Existing Convolutional Neural Network (CNN) or Transformer models struggle with incomplete feature representation and boundary detail loss.

Purpose of the Study:

To develop an innovative hybrid architecture for improved camouflaged object detection.
To enhance feature representation and boundary detail accuracy in detecting concealed objects.

Main Methods:

Proposed a hybrid architecture integrating CNNs and Transformers.
Devised a Hybrid Feature Fusion Module (HFFM) to harmonize hierarchical features.
Designed a Combined Recursive Decoder (CRD) for adaptive feature aggregation and multi-scale detail capture.

Keywords:

camouflaged object detection complementary information strategy edge refinement feature fusion strategy

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Introduced a Foreground-Background Selection (FBS) module for progressive contour refinement and background suppression.

Main Results:

The hybrid architecture achieved state-of-the-art performance on four public COD datasets (CHAMELEON, CAMO, COD10K, NC4K).
The proposed modules (HFFM, CRD, FBS) significantly boosted representational quality and structural detail capture.
The method demonstrated superior performance in precisely detecting and delineating camouflaged objects.

Conclusions:

The proposed hybrid CNN-Transformer model offers a significant advancement in camouflaged object detection.
The synergistic integration of different network strengths overcomes limitations of individual architectures.
This approach provides a robust solution for accurately identifying objects in complex, ambiguous backgrounds.