Visible-Infrared Camouflaged Object Detection

Although great progress has been made in Camouflaged Object Detection (COD), it still faces challenges in complex real-world scenes. Existing methods are primarily designed for visible images but face limitations when detecting highly camouflaged or partially occluded objects. Integrating multiple complementary information sources, such as visible images and infrared images, is an effective way to improve the performance of COD. However, research in this field is limited by the lack of comprehensive and high-quality benchmark datasets. To solve this problem, a Visible-Infrared Artificial Camouflage (VIAC) dataset is constructed. Building on this dataset, we propose a novel Visible-Infrared Camouflaged Object Detection (VICOD) framework, termed the Confidence-Guided Fusion and Inpainting Network (CGFINet). The network utilizes a cross-modal collaborative fusion module (CMCF) to achieve adaptive integration of visible and infrared information. Simultaneously, low-confidence regions segmentation boundaries are refined by leveraging high-confidence pixel information within the confidence-driven inpainting module (CDIM). To focus on low-confidence areas, pixel-level uncertainty is incorporated into the loss function as a dynamic weight factor, which prompts the model to focus on high-uncertainty areas. Extensive experiments on VIAC demonstrate that our method achieves state-of-the-art performance, surpassing existing COD and visible-infrared SOD approaches.