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

Source: [Exploring Boundary-Aware Spatial-Frequency Fusion for Camouflaged Object Detection](<https://www.sota2.com/research/paper/69e7050b7d0172140ec7f230>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-04-22T05:23:53.567Z

Authors: Song Yu, Yang Hu, Haokang Ding, Zhifang Liao, Yucheng Song

Published: 2026-04-20T06:45:42.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Camouflaged Object Detection on NC4K (test)](<https://www.sota2.com/research/sota/camouflaged-object-detection-on-nc4k-test.md>): Camouflaged Object Detection; NC4K (test); Sm: 0.892.

- [Camouflaged Object Detection on COD10K (test)](<https://www.sota2.com/research/sota/camouflaged-object-detection-on-cod10k-test.md>): Camouflaged Object Detection; COD10K (test); S-measure (S\_alpha): 0.871.

- [Camouflaged Object Detection on CAMO (test)](<https://www.sota2.com/research/sota/camouflaged-object-detection-on-camo-test.md>): Camouflaged Object Detection; CAMO (test); M: 0.037.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2604.17879>)

[PDF](<https://arxiv.org/pdf/2604.17879.pdf>)