用多光谱图像检测隐身物体，效果优于传统方法
Seeing the Unseen: Camouflaged Object Detection Beyond the Visible Spectrum
- 提出MSFormer框架，直接输入多光谱图像生成掩码
- 在COD任务上实现更优性能，显著提升检测精度
- 适合需要突破可见光限制的低可见场景应用
近年来，隐身物体检测（COD）在低可见度场景中取得显著进展，早期研究已成功定位伪装场景中的目标。然而，现有方法主要依赖传统的三通道RGB图像，导致可用视觉信息局限于有限光谱范围。多光谱图像通过捕捉精细的光谱特征，提供更丰富的场景信息。为此，我们提出一种新方法，利用多光谱图像进行COD，构建了一个端到端框架——MSFormer，以多光谱伪装图像为输入，输出二值掩码。此外，我们还提供了多光谱波段融合在该复杂低视觉任务中的实证依据。大量实验表明，该方法显著优于现有技术。
原文摘要 · Abstract (English)
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.
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