发表机构
Indraprastha Institute of Information Technology, Delhi; Delhi Technological University, Delhi（英迪拉普拉斯塔信息技术学院（德里）; 德里理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对现有伪装物体检测仅依赖RGB图像的局限，提出多光谱输入的端到端框架MSFormer，经实验验证其性能优于现有方法。
AI 中文摘要
近期伪装物体检测（COD）的进展在低能见度场景取得显著进步，已有开创性研究成功定位伪装场景中的物体。但现有方法多依赖传统三通道RGB图像，仅能获取有限光谱范围的视觉信息。多光谱图像通过捕获精细光谱特征提供场景的丰富信息，因此我们利用多光谱图像进行COD，提出从对应多光谱输入中检测伪装物体的新方法。具体而言，我们提出端到端框架MSFormer，将多光谱伪装图像作为输入并预测其二值掩码。此外，我们还为该复杂低视觉任务中整合多光谱波段提供实证依据。大量实验表明，我们的方法优于现有方法，具有有效性。
英文摘要
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.
