通过融合频域与空域特征提升隐蔽目标检测精度
Exploring Boundary-Aware Spatial-Frequency Fusion for Camouflaged Object Detection
- 设计双域引导融合机制，结合频域相位信息与空间边缘特征
- 在三个基准数据集上超越现有最优方法，显著提升检测效果
- 适合从事图像分割与隐蔽目标检测的研究者参考
隐蔽目标检测因目标与背景高度相似而困难。现有方法主要依赖空域边缘提取和局部像素信息，忽视全局结构特征，且未有效利用频域特征中的相位谱信息。为此，我们提出BASFNet框架，基于边界感知的频域与空域融合。该方法采用频域与空域特征的双重引导集成：引入基于相位谱的频域增强边缘探索模块（FEEM）和空间核心分割模块（SCSM），联合捕捉隐蔽目标的边界与物体特征；并通过空间-频率融合交互模块（SFFIM）实现高效融合；同时采用边界感知训练策略优化边界检测。在三个基准数据集上，BASFNet均优于现有最先进方法，验证了频域与空域信息融合在隐蔽目标检测任务中的有效性。
原文摘要 · Abstract (English)
Camouflaged Object Detection is challenging due to the high degree of similarity between camouflaged objects and their surrounding backgrounds. Current COD methods mainly rely on edge extraction in the spatial domain and local pixel-level information, neglecting the importance of global structural features. Additionally, they fail to effectively leverage the importance of phase spectrum information within frequency domain features. To this end, we propose a COD framework BASFNet based on boundary-aware frequency domain and spatial domain fusion.This method uses dual guided integration of frequency domain and spatial domain features. A phase-spectrum-based frequency-enhanced edge exploration module (FEEM) and a spatial core segmentation module (SCSM) are introduced to jointly capture the boundary and object features of camouflaged objects. These features are then effectively integrated through a spatial-frequency fusion interaction module (SFFIM). Furthermore, the boundary detection is further optimized through an boundary-aware training strategy. BASFNet outperforms existing state-of-the-art methods on three benchmark datasets, validating the effectiveness of the fusion of frequency and spatial domain information in COD tasks.
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