Edge-Guided Pixel Level Connected Component Assisted Camouflaged Object Detection

Abstract

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Due to the inherent visual similarity between the camouflaged object and background, camouflaged object detection (COD) is widely recognized as a challenging task in the field of computer vision, and traditional object detection networks often struggle to extract features and accurately identify camouflaged objects. In this paper, we propose an edge-guided pixel level connected component assisted network for COD. Specifically, the edge prior is used to guide object feature extraction and the pixel level connected component obtained from the extracted feature is used to refine the bounding box of the object. We selectively employ a gray-polarization COD dataset to showcase the ability of feature extraction from backgrounds where camouflaged objects may blend in or be occluded. Numerous experiments demonstrate the superiority of our method compared to state-of-the-arts in the case of limited information.