Language-Guided Structure-Aware Network for Camouflaged Object Detection
Summary: arXiv:2603.24355v1 Announce Type: cross
Abstract
Camouflaged Object Detection (COD) aims to segment objects that are highly integrated with the background in terms of color, texture, and structure, making it a highly challenging task in computer vision. Although existing methods introduce multi-scale fusion and attention mechanisms to alleviate the above issues, they generally lack the guidance of textual semantic priors, which limits the model’s ability to focus on camouflaged regions in complex scenes. To address this issue, this paper proposes a Language-Guided Structure-Aware Network (LGSAN).
Introduction
The detection of camouflaged objects poses significant challenges in the field of computer vision. Traditional methods often struggle to differentiate between objects and their backgrounds due to similarities in color, texture, and structure. Recent advancements in deep learning have introduced various techniques to enhance object detection capabilities; however, many of these approaches fail to leverage the potential of language as a guiding factor, resulting in limited performance.
Proposed Methodology
This study introduces the Language-Guided Structure-Aware Network (LGSAN) to improve the detection of camouflaged objects. The proposed framework consists of several innovative components:
- Visual Backbone: The model is built upon the PVT-v2 backbone, which serves as a foundation for extracting visual features.
- CLIP Integration: By incorporating CLIP, the model generates masks from text prompts and RGB images, effectively guiding the multi-scale features to concentrate on potential target regions.
- Fourier Edge Enhancement Module (FEEM): This module integrates multi-scale features with high-frequency information from the frequency domain, enhancing edge features essential for object detection.
- Structure-Aware Attention Module (SAAM): This module improves the model’s understanding of object structures and boundaries, facilitating better detection outcomes.
- Coarse-Guided Local Refinement Module (CGLRM): This component enhances the fine-grained reconstruction and boundary integrity of camouflaged object regions.
Results and Performance
Extensive experiments were conducted to evaluate the performance of the LGSAN across multiple COD datasets. The results consistently demonstrate that the proposed method achieves highly competitive performance compared to existing state-of-the-art approaches.
Key findings include:
- Improved accuracy in detecting camouflaged objects in complex scenes.
- Enhanced robustness against various background textures and colors.
- Significant reduction in false positive rates, leading to more reliable detection outcomes.
Conclusion
The Language-Guided Structure-Aware Network presents a significant advancement in the field of camouflaged object detection. By effectively integrating language guidance and advanced feature extraction techniques, LGSAN outperforms existing methods and offers a robust solution to the challenges posed by camouflaged objects. Future research may explore further enhancements and applications of this innovative framework in various domains of computer vision.