Exploring Boundary-Aware Spatial-Frequency Fusion for Camouflaged Object Detection

TL;DR

This paper introduces BASFNet, a novel framework for camouflaged object detection that fuses frequency and spatial domain features using boundary-aware strategies, improving detection accuracy.

Contribution

The paper proposes a dual-guided fusion framework combining frequency and spatial features with boundary-aware training for enhanced camouflaged object detection.

Findings

BASFNet outperforms existing methods on three benchmark datasets.

The frequency-enhanced edge exploration module improves boundary detection.

Fusion of frequency and spatial features enhances overall detection performance.

Abstract

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.