pith. sign in

arxiv: 2604.17879 · v1 · submitted 2026-04-20 · 💻 cs.CV

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

Song Yu , Yang Hu , Haokang Ding , Zhifang Liao , Yucheng Song This is my paper

Pith reviewed 2026-05-10 04:46 UTC · model grok-4.3

classification 💻 cs.CV
keywords camouflaged object detectionfrequency domainspatial domain fusionboundary awarenessphase spectrumimage segmentationcomputer visiondeep learning
0
0 comments X

The pith

Boundary-aware fusion of frequency phase spectra and spatial features detects camouflaged objects more accurately.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper aims to show that camouflaged object detection improves when global structural information from the frequency domain, especially phase spectra, is combined with local spatial details under boundary guidance. Current approaches focus too narrowly on edge extraction in space and miss complementary cues that frequency analysis can provide. If the fusion works as described, detectors would handle scenes where objects and backgrounds share similar textures and colors more reliably. The authors support this by introducing dedicated modules for edge exploration in frequency, core segmentation in space, and their interaction, plus a training strategy that emphasizes boundaries, and report stronger results than prior methods on three benchmarks.

Core claim

The authors present BASFNet as a framework that uses a phase-spectrum-based frequency-enhanced edge exploration module to capture global boundary cues, a spatial core segmentation module to extract local object information, and a spatial-frequency fusion interaction module to integrate the two streams, with further refinement via boundary-aware training. This setup is claimed to address the limitations of purely spatial methods by leveraging complementary frequency-domain information for better discrimination of camouflaged objects.

What carries the argument

The boundary-aware spatial-frequency fusion process, carried out by the FEEM, SCSM, and SFFIM modules together with the boundary-aware training strategy.

If this is right

  • Detection accuracy rises on the three standard COD benchmarks because global phase cues help separate objects that match their backgrounds locally.
  • Boundary precision improves as the training strategy directly optimizes edge quality alongside object segmentation.
  • The dual-domain integration supplies cues that neither domain provides alone, enabling more complete object masks in complex environments.
  • The overall approach validates that frequency information, when guided by boundaries, adds value beyond what spatial-only pipelines achieve in COD.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same fusion pattern could be tested on other low-contrast segmentation problems such as medical lesion detection or industrial defect inspection.
  • Phase-spectrum emphasis might generalize to tasks where subtle global structure distinguishes targets from clutter, even if local pixels look identical.
  • Adding explicit boundary supervision during fusion may reduce over-segmentation in real-world images with gradual transitions.
  • The modules could be inserted into existing COD architectures to measure whether the performance lift holds without full retraining.

Load-bearing premise

That frequency-domain phase information and spatial-domain features supply genuinely complementary boundary and object cues that the modules can combine without creating new errors on camouflaged scenes outside the training distribution.

What would settle it

Evaluating the full model on a held-out set of camouflaged scenes with novel texture matches or lighting conditions and finding that accuracy falls to or below the level of strong spatial-only baselines.

Figures

Figures reproduced from arXiv: 2604.17879 by Haokang Ding, Song Yu, Yang Hu, Yucheng Song, Zhifang Liao.

Figure 1
Figure 1. Figure 1: Frequency domain analysis of camouflaged images involves frequency decomposition and reconstruction. The first row, labeled "ORI Image," shows the original image, while the second row, "PSR Image," displays the reconstructed image containing only phase information. pixel-level edge features of camouflaged objects, these methods as￾sist the model in more accurately identifying the target. However, the core … view at source ↗
Figure 2
Figure 2. Figure 2: Overall architecture of the proposed framework BASFNet. The framework consists of three key modules: FEEM, SCSM, and SFFIM, each of which utilizes enhanced feature fusion block (EFFB) for effective feature integration. EFFB enhances the outputs of these modules by leveraging their complementary strengths to boost overall performance. discrimination. To address this issue, we propose a phase spectrum￾based … view at source ↗
Figure 3
Figure 3. Figure 3: The process of producing edge ground truths in boundary-aware enhancement strategy. where SpaOut(·) is the output projection module that restores the dimension to the original feature dimension, and ASP P (·) is the atrous spatial pyramid pooling module that captures multi-scale con￾textual information. 3.4 Spatial-Frequency Fusion Interaction Module As shown in [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Illustration of Enhanced Feature Fusion Block. extracts rich contextual features through parallel convolution oper￾ations and employs an Enhance Attention module to guide the net￾work’s focus toward more discriminative semantic regions. It selec￾tively strengthens key feature channels, thereby improving the se￾mantic completeness and discriminative power of the fused features. Specifically, EFFB first conc… view at source ↗
Figure 5
Figure 5. Figure 5: Visual comparisons of our BIRNet and the competing methods. final prediction Pf inal, we apply the weighted binary cross-entropy loss (L w BCE) and weighted IOU loss (L w IOU ) to handle class imbal￾ance and improve segmentation accuracy: L init seg = L w BCE (Pinit, Gm) + L w IOU (Pinit, Gm) (14) L f inal seg = L w BCE (Pf inal, Gm) + L w IOU (Pf inal, Gm) (15) where Gm is the ground truth mask for the ca… view at source ↗
read the original 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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

0 major / 4 minor

Summary. The manuscript proposes BASFNet, a camouflaged object detection (COD) framework that fuses boundary-aware frequency-domain and spatial-domain features. It introduces a phase-spectrum-based frequency-enhanced edge exploration module (FEEM), a spatial core segmentation module (SCSM), a spatial-frequency fusion interaction module (SFFIM), and a boundary-aware training strategy to capture complementary global structural and local boundary cues. The central claim is that this dual-domain integration outperforms existing state-of-the-art methods on three standard COD benchmark datasets.

Significance. If the reported gains hold under rigorous evaluation, the work would demonstrate the value of explicitly incorporating phase-spectrum frequency information alongside spatial features for COD, addressing a gap in current spatial-only or edge-focused approaches. The modular design supports targeted ablations and could inform future fusion strategies in related detection tasks.

minor comments (4)
  1. The abstract states outperformance on three benchmarks but does not include any quantitative metrics, dataset names, or baseline comparisons; move key results (e.g., mIoU or F-measure tables) into the abstract or a prominent early table for immediate verifiability.
  2. Notation for the modules (FEEM, SCSM, SFFIM) is introduced without an accompanying diagram or equation block showing their internal data flow and tensor dimensions; add a single overview figure with labeled inputs/outputs.
  3. The boundary-aware training strategy is described at a high level; specify the exact loss formulation (e.g., weighted BCE or Dice) and its weighting hyper-parameters in the methods section.
  4. No error analysis or failure-case visualization is mentioned; include at least one qualitative figure showing cases where prior SOTA fails but BASFNet succeeds, with corresponding quantitative per-image metrics.

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for the positive summary of our manuscript and the recommendation for minor revision. The recognition that our boundary-aware spatial-frequency fusion approach addresses a gap in current COD methods by leveraging phase-spectrum information is appreciated. As no specific major comments were raised in the report, we provide no point-by-point responses below but remain ready to incorporate any minor clarifications or adjustments in the revised version.

Circularity Check

0 steps flagged

No significant circularity; empirical architecture with external validation

full rationale

The paper proposes an empirical neural architecture (BASFNet) for camouflaged object detection consisting of FEEM, SCSM, SFFIM modules and boundary-aware training. No derivation chain, equations, fitted parameters, or self-citation load-bearing steps are present in the abstract or described method. Claims rest on performance measured against external benchmark datasets rather than any internal reduction to inputs by construction. This is a standard design-and-evaluate CV paper whose central claim remains falsifiable outside the paper itself.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

No mathematical derivations, free parameters, or new physical entities are introduced; the contribution is an architectural design whose validity rests on empirical performance.

pith-pipeline@v0.9.0 · 5488 in / 1001 out tokens · 27337 ms · 2026-05-10T04:46:58.328393+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

42 extracted references · 42 canonical work pages · 1 internal anchor

  1. [1]

    N. U. Bhajantri and P. Nagabhushan. Camouflage defect identification: a novel approach. In9th International Conference on Information Tech- nology (ICIT’06), pages 145–148. IEEE, 2006

    work page 2006

  2. [2]

    J. Canny. A computational approach to edge detection.IEEE Transac- tions on pattern analysis and machine intelligence, (6):679–698, 1986

    work page 1986

  3. [3]

    R. Cong, M. Sun, S. Zhang, X. Zhou, W. Zhang, and Y . Zhao. Frequency perception network for camouflaged object detection. InProceedings of the 31st ACM International Conference on Multimedia, pages 1179– 1189, 2023

    work page 2023

  4. [4]

    An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

    A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, et al. An image is worth 16x16 words: Transformers for image recognition at scale.arXiv preprint arXiv:2010.11929, 2020

    work page internal anchor Pith review Pith/arXiv arXiv 2010

  5. [5]

    Fan, M.-M

    D.-P. Fan, M.-M. Cheng, Y . Liu, T. Li, and A. Borji. Structure-measure: A new way to evaluate foreground maps. InProceedings of the IEEE international conference on computer vision, pages 4548–4557, 2017

    work page 2017

  6. [6]

    D.-P. Fan, C. Gong, Y . Cao, B. Ren, M.-M. Cheng, and A. Borji. Enhanced-alignment measure for binary foreground map evaluation. arXiv preprint arXiv:1805.10421, 2018

    work page arXiv 2018

  7. [7]

    Fan, G.-P

    D.-P. Fan, G.-P. Ji, G. Sun, M.-M. Cheng, J. Shen, and L. Shao. Cam- ouflaged object detection. InProceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 2777–2787, 2020

    work page 2020

  8. [8]

    Fan, G.-P

    D.-P. Fan, G.-P. Ji, T. Zhou, G. Chen, H. Fu, J. Shen, and L. Shao. Pranet: Parallel reverse attention network for polyp segmentation. In International conference on medical image computing and computer- assisted intervention, pages 263–273. Springer, 2020

    work page 2020

  9. [9]

    Fan, G.-P

    D.-P. Fan, G.-P. Ji, M.-M. Cheng, and L. Shao. Concealed object detec- tion.IEEE transactions on pattern analysis and machine intelligence, 44(10):6024–6042, 2021

    work page 2021

  10. [10]

    K. Han, Y . Wang, H. Chen, X. Chen, J. Guo, Z. Liu, Y . Tang, A. Xiao, C. Xu, Y . Xu, et al. A survey on vision transformer.IEEE transactions on pattern analysis and machine intelligence, 45(1):87–110, 2022

    work page 2022

  11. [11]

    C. He, K. Li, Y . Zhang, L. Tang, Y . Zhang, Z. Guo, and X. Li. Camou- flaged object detection with feature decomposition and edge reconstruc- tion. InProceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 22046–22055, 2023

    work page 2023

  12. [12]

    Huang, H

    Z. Huang, H. Dai, T.-Z. Xiang, S. Wang, H.-X. Chen, J. Qin, and H. Xiong. Feature shrinkage pyramid for camouflaged object detec- tion with transformers. InProceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 5557–5566, 2023

    work page 2023

  13. [13]

    Huerta, D

    I. Huerta, D. Rowe, M. Mozerov, and J. Gonzàlez. Improving back- ground subtraction based on a casuistry of colour-motion segmentation problems. InIberian conference on pattern recognition and image anal- ysis, pages 475–482. Springer, 2007

    work page 2007

  14. [14]

    Hwang and J

    K.-S. Hwang and J. Ma. Military camouflaged object detection with deep learning using dataset development and combination.The Journal of Defense Modeling and Simulation, page 15485129241233299, 2024

    work page 2024

  15. [15]

    Kavitha, B

    C. Kavitha, B. P. Rao, and A. Govardhan. An efficient content based image retrieval using color and texture of image sub blocks.Interna- tional Journal of Engineering Science and Technology (IJEST), 3(2): 1060–1068, 2011

    work page 2011

  16. [16]

    T.-N. Le, T. V . Nguyen, Z. Nie, M.-T. Tran, and A. Sugimoto. Anabranch network for camouflaged object segmentation.Computer vision and image understanding, 184:45–56, 2019

    work page 2019

  17. [17]

    S. Li, X. Li, Z. Li, H. Ma, J. Sheng, and B. Li. Dual guidance enhancing camouflaged object detection via focusing boundary and localization representation. In2024 IEEE International Conference on Multimedia and Expo (ICME), pages 1–6. IEEE, 2024

    work page 2024

  18. [18]

    Liang, G

    Y . Liang, G. Qin, M. Sun, X. Wang, J. Yan, and Z. Zhang. A systematic review of image-level camouflaged object detection with deep learning. Neurocomputing, 566:127050, 2024

    work page 2024

  19. [19]

    Z. Liu, Y . Lin, Y . Cao, H. Hu, Y . Wei, Z. Zhang, S. Lin, and B. Guo. Swin transformer: Hierarchical vision transformer using shifted win- dows. InProceedings of the IEEE/CVF international conference on computer vision, pages 10012–10022, 2021

    work page 2021

  20. [20]

    Y . Lv, J. Zhang, Y . Dai, A. Li, B. Liu, N. Barnes, and D.-P. Fan. Si- multaneously localize, segment and rank the camouflaged objects. In Proceedings of the IEEE/CVF conference on computer vision and pat- tern recognition, pages 11591–11601, 2021

    work page 2021

  21. [21]

    Margolin, L

    R. Margolin, L. Zelnik-Manor, and A. Tal. How to evaluate foreground maps? InProceedings of the IEEE conference on computer vision and pattern recognition, pages 248–255, 2014

    work page 2014

  22. [22]

    A. V . Oppenheim and J. S. Lim. The importance of phase in signals. Proceedings of the IEEE, 69(5):529–541, 1981

    work page 1981

  23. [23]

    Y . Pang, X. Zhao, T.-Z. Xiang, L. Zhang, and H. Lu. Zoom in and out: A mixed-scale triplet network for camouflaged object detection. InProceedings of the IEEE/CVF Conference on computer vision and pattern recognition, pages 2160–2170, 2022

    work page 2022

  24. [24]

    Perazzi, P

    F. Perazzi, P. Krähenbühl, Y . Pritch, and A. Hornung. Saliency filters: Contrast based filtering for salient region detection. In2012 IEEE con- ference on computer vision and pattern recognition, pages 733–740. IEEE, 2012

    work page 2012

  25. [25]

    Pérez-de la Fuente, X

    R. Pérez-de la Fuente, X. Delclòs, E. Peñalver, M. Speranza, J. Wierz- chos, C. Ascaso, and M. S. Engel. Early evolution and ecology of cam- ouflage in insects.Proceedings of the National Academy of Sciences, 109(52):21414–21419, 2012

    work page 2012

  26. [26]

    T. H. Phung, H.-J. Chen, and H.-H. Shuai. Hierarchically aggregated identification transformer network for camouflaged object detection. In 2024 IEEE International Conference on Multimedia and Expo (ICME), pages 1–6. IEEE, 2024

    work page 2024

  27. [27]

    Siricharoen, S

    P. Siricharoen, S. Aramvith, T. H. Chalidabhongse, and S. Siddhichai. Robust outdoor human segmentation based on color-based statistical approach and edge combination. InThe 2010 international conference on green circuits and systems, pages 463–468. IEEE, 2010

    work page 2010

  28. [28]

    Z. Song, X. Kang, X. Wei, H. Liu, R. Dian, and S. Li. Fsnet: Focus scanning network for camouflaged object detection.IEEE Transactions on Image Processing, 32:2267–2278, 2023

    work page 2023

  29. [29]

    Y . Sun, S. Wang, C. Chen, and T.-Z. Xiang. Boundary-guided camou- flaged object detection.arXiv preprint arXiv:2207.00794, 2022

    work page arXiv 2022

  30. [30]

    Y . Sun, C. Xu, J. Yang, H. Xuan, and L. Luo. Frequency-spatial entan- glement learning for camouflaged object detection. InEuropean Con- ference on Computer Vision, pages 343–360. Springer, 2024

    work page 2024

  31. [31]

    Tankus and Y

    A. Tankus and Y . Yeshurun. Convexity-based visual camouflage break- ing.Computer Vision and Image Understanding, 82(3):208–237, 2001

    work page 2001

  32. [32]

    J. Tong, Y . Bi, C. Zhang, H. Bi, and Y . Yuan. Local to global purifi- cation strategy to realize collaborative camouflaged object detection. Computer Vision and Image Understanding, 241:103932, 2024

    work page 2024

  33. [33]

    X. Wu, C. Zhan, Y .-K. Lai, M.-M. Cheng, and J. Yang. Ip102: A large- scale benchmark dataset for insect pest recognition. InProceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 8787–8796, 2019

    work page 2019

  34. [34]

    J. Xiao, T. Chen, X. Hu, G. Zhang, and S. Wang. Boundary-guided context-aware network for camouflaged object detection.Neural Com- puting and Applications, 35(20):15075–15093, 2023

    work page 2023

  35. [35]

    H. Yang, Y . Zhu, K. Sun, H. Ding, and X. Lin. Camouflaged object de- tection via dual-branch fusion and dual self-similarity constraints.Pat- tern Recognition, 157:110895, 2025

    work page 2025

  36. [36]

    J. Yang, Q. Zhang, Y . Zhao, Y . Li, and Z. Liu. Bi-directional boundary- object interaction and refinement network for camouflaged object detec- tion. In2024 IEEE International Conference on Multimedia and Expo (ICME), pages 1–6. IEEE, 2024

    work page 2024

  37. [37]

    B. Yin, X. Zhang, D.-P. Fan, S. Jiao, M.-M. Cheng, L. Van Gool, and Q. Hou. Camoformer: Masked separable attention for camouflaged ob- ject detection.IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024

    work page 2024

  38. [38]

    Z. Yu, X. Zhang, L. Zhao, Y . Bin, and G. Xiao. Exploring deeper! segment anything model with depth perception for camouflaged object detection. InProceedings of the 32nd ACM International Conference on Multimedia, pages 4322–4330, 2024

    work page 2024

  39. [39]

    Q. Zhai, X. Li, F. Yang, C. Chen, H. Cheng, and D.-P. Fan. Mutual graph learning for camouflaged object detection. InProceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 12997–13007, 2021

    work page 2021

  40. [40]

    Q. Zhai, X. Li, F. Yang, Z. Jiao, P. Luo, H. Cheng, and Z. Liu. Mgl: Mutual graph learning for camouflaged object detection.IEEE Trans- actions on Image Processing, 32:1897–1910, 2022

    work page 1910

  41. [41]

    Zhang, D

    S. Zhang, D. Kong, Y . Xing, Y . Lu, L. Ran, G. Liang, H. Wang, and Y . Zhang. Frequency-guided spatial adaptation for camouflaged object detection.IEEE Transactions on Multimedia, 27:72–83, 2025

    work page 2025

  42. [42]

    H. Zhu, P. Li, H. Xie, X. Yan, D. Liang, D. Chen, M. Wei, and J. Qin. I can find you! boundary-guided separated attention network for cam- ouflaged object detection. InProceedings of the AAAI conference on artificial intelligence, volume 36, pages 3608–3616, 2022

    work page 2022