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Seeing the Unseen: Camouflaged Object Detection Beyond the Visible Spectrum

Avi Gupta, Trasha Gupta

arXiv:2608.30355v1cs.CV

TL;DR

Existing COD methods largely rely on three-channel RGB images, limiting access to spectral cues that can distinguish camouflaged objects. MSFormer adapts a transformer-based encoder–decoder to multispectral inputs and predicts binary masks, with experiments reporting stronger performance across RGB, multispectral, and hyperspectral settings.

  • Problem

    Existing COD approaches predominantly rely on visible RGB imagery, while multispectral information can provide spectral cues beyond the visible range.

  • Method

    MSFormer is an end-to-end transformer-based framework that uses weight inflation, projected multiscale features, and a cascaded decoder to predict binary masks from multispectral inputs.

  • Results

    MSFormer outperforms prior methods on the MCOD benchmark and performs strongly across RGB, multispectral, and hyperspectral COD settings.

  • Takeaways & Limitations

    Multispectral inputs provide useful information for COD, with MSI maintaining advantages over RGB even when trained with fewer images.

Abstract

from arXiv · show

Recent advances in camouflaged object detection (COD) have led to substantial progress in challenging low-visibility scenarios, with pioneering studies demonstrating notable success in localizing objects in camouflaged scenes. Despite these achievements, existing approaches predominantly rely on conventional three-channel RGB imagery, thereby constraining the available visual information to a limited spectral range. Multispectral images offer a wide range of information about a scene by capturing fine-grained spectral signatures. Hence, by leveraging multispectral images for COD, we introduce a novel approach to detect camouflaged objects from the corresponding multispectral inputs. In particular, we propose an end-to-end framework, \textbf{\textit{MSFormer}}, that takes a multispectral camouflaged image as input and predicts a binary mask for it. Additionally, we also provide empirical justification for integrating multispectral bands for this complex low-vision task. Our extensive experiments demonstrate the effectiveness of our method, which outperforms existing methods.

1. Introduction

Camouflaged object detection is difficult because foregrounds resemble their surroundings, while conventional methods remain limited to visible RGB information. MSFormer addresses this gap by adapting transformer-based segmentation to multispectral inputs and fusing complementary spectral and multiscale features.

  • Motivation: Camouflaged objects resemble their surroundings, making standard segmentation methods ineffective in ambiguous environments.The task is relevant to applications including wildlife conservation and medical polyp segmentation.
  • Motivation: Traditional COD methods are constrained by the visible RGB spectrum, whereas multispectral imaging captures information imperceptible in visible bands.The supplied passages describe multispectral imagery as providing more diverse scene information than traditional RGB images.
  • Motivation: In an example, object and background reflectance overlap across visible bands but diverge sharply in near-infrared bands.The example identifies S1–S6 as visible bands and S7–S8 as near-infrared bands.
  • Research gap: Multispectral COD remains an early-stage research area, and effectively fusing multispectral data into RGB-centric backbones remains an open challenge.Existing approaches using band-specific learnable layers are described as data-inefficient and prone to overfitting.
  • Proposed approach: MSFormer is an end-to-end transformer framework that predicts binary masks from multispectral camouflaged images.Its design includes weight inflation for multispectral inputs, feature projection, and a cascaded decoder for multiscale prediction.

2. Related Works

Related work spans RGB camouflaged-object detection, multispectral segmentation, and hyperspectral camouflage detection. These lines of research motivate incorporating non-visible spectral information into COD.

  • Camouflaged object detection: Camouflaged object detection targets objects that closely match their surroundings in appearance.
  • Camouflaged object detection: Recent COD methods use deep multiscale features, biologically inspired strategies, visual supervision, and foundation-model adaptations.The cited foundation-model work adapts models with wavelet-based transformations for challenging camouflage scenarios.
  • Multispectral segmentation: Multispectral image segmentation integrates spectral information such as thermal or infrared data to address limitations of RGB models.Prior work includes dual-encoder and two-stage fusion designs for modality integration.
  • Hyperspectral camouflage detection: Several studies have focused on hyperspectral methods for camouflage detection in multispectral imagery.
  • Multispectral motivation: Statistical motivation for multispectral COD includes higher NIR contrast, heavier separability tails, lower visible–NIR correlation, and higher NIR separability scores.The figure describes these properties across contrast, feature separability, correlation, and spectral-landscape analyses.

3. Methodology

The methodology empirically evaluates whether multispectral bands provide discriminative information beyond RGB by measuring contrast, separability, and inter-channel information structure. The analyses identify stronger and less redundant signals in NIR bands.

  • Statistical motivation: The analysis compares RGB with full multispectral representations using optical contrast, feature separability, and information orthogonality.
  • Optical contrast and spectral divergence: The study quantifies camouflage using Michelson contrast computed for each spectral band across the dataset.Visible and NIR bands are compared to assess foreground–background contrast.
  • Optical contrast and spectral divergence: 2× higher median contrast occurs in NIR bands S7–S8 than in visible bands, indicating stronger spectral divergence beyond the human visual range.The visible bands discussed are S5, S3, and S2.
  • Feature separability: NIR bands form a separability ridge across samples, while RGB forms a consistently challenging valley.The spectral landscape indicates that the NIR discriminative signal is consistent across diverse samples.
  • Feature separability: RGB separability concentrates near J ≈0.2, whereas MSI extends beyond J > 5, indicating stronger object–background discrimination.The passage characterizes this as shifting from low-contrast semantic inference toward high-contrast feature discrimination.
  • Information orthogonality: Visible bands show high collinearity with ρ > 0.82, while visible–NIR correlations drop significantly, indicating non-redundant spectral information.

3.2. Overview

MSFormer processes multispectral images through a hierarchical encoder–decoder pipeline. Multiscale features are projected to uniform dimensions, recursively fused, and upsampled into prediction maps supervised by segmentation loss.

  • Overview: MSFormer takes a multispectral image with Cms spectral channels and predicts a pixelwise binary segmentation map.The input has spatial dimensions H × W, and each output pixel represents the probability of belonging to a camouflaged object.
  • Overview: The image encoder extracts multiscale features from the multispectral input.
  • Overview: Feature Projection Layers standardize extracted hierarchical features to a uniform channel dimension.
  • Overview: The decoder produces prediction maps at multiple scales, and segmentation loss compares them with the ground-truth mask.
  • Overview: The Cascaded Decoder Module progressively aggregates uniform features in a top-down pathway while recovering spatial resolution.The decoder integrates semantic context from deeper layers during reconstruction.

3.3. Multispectral Feature Extractor

The multispectral feature extractor adapts a pyramid vision transformer to multispectral inputs while preserving pretrained RGB representations and producing hierarchical features.

  • 3.3. Multispectral Feature Extractor: The pyramid vision transformer encoder captures long-range dependencies and robust spectral features for dense prediction.Its pyramid structure supports extraction of multiscale representations.
  • 3.3. Multispectral Feature Extractor: Weight inflation maps pretrained RGB weights to corresponding channels and initializes additional spectral channels with their RGB-channel arithmetic mean.This preserves learned RGB representations while providing balanced initialization for novel spectral bands.
  • 3.3. Multispectral Feature Extractor: The encoder extracts four hierarchical feature maps with progressively reduced spatial resolution and channel dimensions 64, 128, 320, and 512.The feature maps are denoted Xi and use channel dimensions from C ∈ {64, 128, 320, 512}.
  • 3.3. Multispectral Feature Extractor: The weight inflation strategy can also be adapted to n-channel bands in the hyperspectral domain.

3.4. Cascaded Decoder Module

The Cascaded Decoder Module progressively fuses standardized hierarchical features through a top-down pathway to combine semantic localization with boundary detail.

  • 3.4. Cascaded Decoder Module: Feature Projection Layers map extracted multiscale features to a uniform channel dimension before decoder fusion.The projection operation uses batch normalization, ReLU, and a 3×3 convolution to resize each feature to 64 channels.
  • 3.4. Cascaded Decoder Module: The decoder starts from the deepest feature and recursively applies upsampling and channel-wise concatenation across stages.Yi denotes the decoded feature at stage i.
  • 3.4. Cascaded Decoder Module: The cascaded design uses deeper semantic cues to guide refinement of shallower, detail-rich features.
  • 3.4. Cascaded Decoder Module: The hierarchical decoder features are upsampled to restore spatial resolution and produce corresponding hierarchical masks.A convolution operation is used before obtaining the hierarchical masks.

3.5. Objective Function

MSFormer uses multiscale deep supervision to train prediction maps at different resolutions with a segmentation loss combining weighted BCE and weighted IoU terms.

  • 3.5. Objective Function: Deep supervision evaluates camouflaged-object prediction maps at three scales, from finest m1 to coarsest m3.Each prediction map is a single-channel probability map.
  • 3.5. Objective Function: The total training objective sums the losses associated with each prediction scale.The ground-truth target is the binary mask mc.
  • 3.5. Objective Function: The segmentation loss combines Weighted Binary Cross-Entropy and Weighted Intersection-over-Union losses to address class imbalance in camouflaged scenes.
  • 3.5. Objective Function: Multiscale supervision trains deeper layers for global object localization while refining fine-grained boundaries at shallower layers.

4.1. Experimental Details

Experiments evaluate MSFormer on multispectral and hyperspectral COD datasets using standard segmentation metrics, with comparisons including MCOD benchmark tables under different inputs.

  • 4.1. Experimental Details: MCOD contains 1,527 eight-channel multispectral images, divided into 1,027 training and 500 test images.HyperCOD adds 350 images with 200 spectral bands, split into 280 training and 70 testing samples.
  • 4.1. Experimental Details: Evaluation uses Mean Absolute Error, mean F-measure, adaptive F-measure, S-measure, and Enhanced Alignment Measure.
  • 4.1. Experimental Details: The implementation uses a pretrained PVT-V2 backbone, AdamW optimization, an initial learning rate of 5e-5, batch size 8, and 65 epochs.Images are resized to 512 × 512 and augmented with flips, mirroring, and rotations.
  • 4.1. Experimental Details: Table 1 compares methods on MCOD, while larger or smaller values are preferred according to each metric’s direction and best results are bolded.The table results are excerpted from [21].
  • 4.1. Experimental Details: Table 2 compares RGB and multispectral inputs on the MCOD benchmark using the same larger-is-better or smaller-is-better metric convention.Its results are also excerpted from [21].

4.2. Experimental Results & Further Analysis

Experiments show that MSFormer benefits from multispectral information across MCOD, traditional RGB, and HyperCOD benchmarks, while ablations identify complementary architectural and training components.

  • Comparison with State-of-the-Art: MSFormer achieves Fβ=0.805 on MCOD, outperforming prior work by approximately 15% on Fβ and approximately 2% on Eξ, Sm, and M.The method also provides more accurate object localization and finer details according to the reported comparison.
  • Traditional COD Benchmarks: On traditional RGB benchmarks, MSFormer reaches Fβ=0.866 on CAMO and Fβ=0.835 on COD10K, although the reported differences are marginal and MCSWA-Net leads on Sm.MSFormer attains the best score on three of four metrics on both CAMO and COD10K.
  • Robustness on Hyperspectral Data: On HyperCOD, MSFormer attains the best Eξ=0.891 and αF=0.692, while HSC-SAM records lower MAE and higher Sm.The results support competitive transfer from multispectral to hyperspectral camouflage-object detection, with complementary metric strengths.
  • Spectral Band Analysis: Single-band inputs degrade performance across every channel, whereas removing any one of the eight bands preserves high, nearly flat performance.NIR bands S7–S8 provide the strongest single-band results, while visible bands S1–S6 remain lowest.
  • Architectural Ablation: Combining feature projection layers with the cascaded decoder achieves Eξ=0.972, Sm=0.881, Fβ=0.805, and M=0.002, outperforming either component alone.The decoder alone produces larger gains than projection layers alone, while projection layers add their largest marginal improvement to Fβ and M when CDM is enabled.
  • Data Generalization: Multispectral input consistently improves MSFormer over visible-only RGB input across training-set sizes and evaluation metrics.With 103 training images, MSI raises Fβ from 0.420 to 0.557 and Sm from 0.672 to 0.745; with all 1,027 images, the gaps narrow to 13.9% and 6.9%.

5. Conclusion

MSFormer is an end-to-end transformer-based framework for multispectral camouflaged object detection. It integrates hierarchical encoder features through a cascaded decoder and performs well across RGB, multispectral, and hyperspectral COD benchmarks.

  • MSFormer performs multispectral camouflaged object detection using an end-to-end transformer-based approach.
  • The method incorporates multispectral inputs into a pyramid vision transformer using an efficient integration strategy.
  • A cascaded decoder integrates hierarchical encoder features and produces prediction maps at multiple scales.
  • The proposed approach performs significantly well across COD benchmarks involving RGB, multispectral, and hyperspectral bands.
  • Future work will extend the approach to multispectral videos.
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