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  1. Home
  3. Research Domains
  5. Information And Computing Sciences
  7. Data Management And Data Science
  9. Query Processing And Optimisation
  11. Sam2-dfbcnet: A Camouflaged Object Detection Network Based On The Heira Architecture Of Sam2.
  1. Home
  3. Research Domains
  5. Information And Computing Sciences
  7. Data Management And Data Science
  9. Query Processing And Optimisation
  11. Sam2-dfbcnet: A Camouflaged Object Detection Network Based On The Heira Architecture Of Sam2.

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SAM2-DFBCNet: A Camouflaged Object Detection Network Based on the Heira Architecture of SAM2.

Cao Yuan1, Libang Liu1, Yaqin Li1

  • 1School of Mathematics and Computer Science, Wuhan Polytechnic University, Wuhan 430040, China.

Sensors (Basel, Switzerland)
|July 30, 2025

View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces SAM2-DFBCNet, a novel network for Camouflaged Object Detection (COD). It significantly improves segmentation accuracy for objects blending into complex backgrounds, outperforming existing methods.

Keywords:
SAM2camouflaged object detectioncontextual awarenessdynamic boundary refinement

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Image Segmentation

Background:

  • Camouflaged Object Detection (COD) faces challenges like low contrast, complex textures, and blurred boundaries.
  • Existing deep learning methods struggle with robust segmentation in these difficult scenarios.

Purpose of the Study:

  • To propose SAM2-DFBCNet, a novel network designed to overcome limitations in current Camouflaged Object Detection techniques.
  • To enhance the accuracy and robustness of segmenting camouflaged objects in challenging visual environments.

Main Methods:

  • Developed SAM2-DFBCNet based on the SAM2 Hiera architecture.
  • Incorporated three key modules: Camouflage-Aware Context Enhancement Module (CACEM), Cross-Scale Feature Interaction Bridge (CSFIB), and Dynamic Boundary Refinement Module (DBRM).
feature fusion
image segmentation
  • Utilized attention mechanisms and bidirectional convolutional GRU for feature fusion and boundary refinement.

    • Main Results:

    • SAM2-DFBCNet outperformed twenty state-of-the-art methods on CAMO, COD10K, and NC4K datasets.
    • Achieved maximum improvements of 7.4% in Sα, 5.78% in Fβ, and 4.78% in Eϕ.
    • Reduced Mean Absolute Error (M) by 37.8%.

    Conclusions:

    • The proposed SAM2-DFBCNet demonstrates superior performance and robustness for Camouflaged Object Detection.
    • The network effectively addresses challenges posed by low contrast, complex textures, and blurred boundaries in camouflage scenarios.