Camouflaged Object Detection Via Style Transfer-Based Data Augmentation

Abstract

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Infrared (IR) images can be seen as complementary to visible light (RGB) images, as they can capture accurate targets in low-visibility conditions. However, camouflaged object detection (COD) based on RGB and IR images is expensive. To this end, we propose to exploit a style transfer-based data augmentation method to generate pseudo-IR images by absorbing the style information of IR images into RGB images, and to perform COD based on RGB and the pseudo-IR images. For RGB and IR-based COD, we propose a novel Edge-guided Uncertainty-aware Fusion Network (EUFNet), to make better use of the complementarity between the two kinds of images. Specifically, an uncertainty-aware fusion module is first proposed to aggregate RGB and IR features by estimating their uncertainties. Then, an edge enhancement module is proposed to extract and enhance the edge information in multiple stages. Lastly, a hierarchical integration module is designed to integrate RGB and IR features with edge cues. Extensive experiments demonstrate the effectiveness of the generated pseudo-IR images as well as the proposed EUFNet. The code is available at https://github.com/csdahunzi/COD.