SFGNet: Semantic and Frequency Guided Network for Camouflaged Object Detection

• URL: http://arxiv.org/abs/2509.11539v2
• Date: Tue, 16 Sep 2025 03:48:17 GMT
• Title: SFGNet: Semantic and Frequency Guided Network for Camouflaged Object Detection
• Authors: Dezhen Wang, Haixiang Zhao, Xiang Shen, Sheng Miao,
• Abstract summary: We propose a novel Semantic and Frequency Guided Network (SFGNet)<n>It incorporates semantic prompts and frequency-domain features to capture camouflaged objects and improve boundary perception.<n>Extensive experiments conducted on three COD benchmark datasets demonstrate that our method significantly outperforms state-of-the-art approaches.
• Score: 2.8563206958455467
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Camouflaged object detection (COD) aims to segment objects that blend into their surroundings. However, most existing studies overlook the semantic differences among textual prompts of different targets as well as fine-grained frequency features. In this work, we propose a novel Semantic and Frequency Guided Network (SFGNet), which incorporates semantic prompts and frequency-domain features to capture camouflaged objects and improve boundary perception. We further design Multi-Band Fourier Module(MBFM) to enhance the ability of the network in handling complex backgrounds and blurred boundaries. In addition, we design an Interactive Structure Enhancement Block (ISEB) to ensure structural integrity and boundary details in the predictions. Extensive experiments conducted on three COD benchmark datasets demonstrate that our method significantly outperforms state-of-the-art approaches. The core code of the model is available at the following link: https://github.com/winter794444/SFGNetICASSP2026.

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