Seeing the Unseen: Camouflaged Object Detection Beyond the Visible Spectrum

2026-08-31

AI Summary

该研究针对伪装物体检测问题，提出了一种利用多光谱图像的新方法MSFormer，以提高在低可见度场景下的物体定位能力。

Abstract

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.

Problem

• camouflaged object detection
• multispectral images
• low-visibility scenarios

Innovation

• multispectral images
• camouflaged object detection (COD)
• end-to-end framework
• MSFormer