期刊论文

Li, JX

英文

Sensors

1424-8220

2025

25

14

4509-

10.3390/s25144509

Conceptualization, C.Y. and L.L.; Methodology, L.L.; Software, C.Y. and L.L.; Validation, Y.L.; Investigation, Y.L.; Data Curation, C.Y. and J.L.; Writing—Original Draft Preparation, C.Y. and L.L.; Writing—Review and Editing, Y.L. and J.L.; Visualization, J.L.; Project Administration, J.L. funding acquisition, J.L. All authors have read and agreed to the published version of the manuscript. This research was funded by Wuhan Polytechnic University (Grant NO. 2025RZ076).

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Camouflaged Object Detection (COD) aims to segment objects that are highly integrated with their background, presenting significant challenges such as low contrast, complex textures, and blurred boundaries. Existing deep learning methods often struggle to achieve robust segmentation under these conditions. To address these limitations, this paper proposes a novel COD network, SAM2-DFBCNet, built upon the SAM2 Hiera architecture. Our network incorporates three key modules: (1) the Camouflage-Aware Context Enhancement Module (CACEM), which fuses ...