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Incremental structural for detection
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AI
DOI：10.1016/j.imavis.2025.105565.png)
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Abstract
En 中文
Camouflaged Object Detection (COD) is a challenging task due to the similarity between camouflaged objects and their backgrounds. Recent approaches predominantly utilize structural cues but often struggle with misinterpretations and noise, particularly for small objects. To address these issues, we propose the Structure-Adaptive Network (SANet), which incrementally supplements structural information from points to surfaces. Our method includes the Key Point Structural Information Prompting Module (KSIP) to enhance point-level structural information, Mixed-Resolution Attention (MRA) to incorporate high-resolution details, and the Structural Adaptation Patch (SAP) to selectively integrate high-resolution patches based on the shape of the camouflaged object. Experimental results on three widely used COD datasets demonstrate that SANet significantly outperforms state-of-the-art methods, achieving more accurate localization and finer edge segmentation, while minimizing background noise. Our code is available at https://github.com/vstar37/ SANet/.
Keywords:
Camouflaged object detection
Image segmentation
Computer vision
Structural information
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