Efficient Camouflaged Object Detection via Progressive Refinement Network
Dongdong Zhang, Chunping Wang, Qiang Fu
Abstract
Camouflaged object detection (COD) aims to identify objects that are perfectly concealed in their surroundings and has attracted increasing attention in recent years. The challenge with COD is the intrinsic similarity between camouflaged objects and background, as well as the weak boundary that often accompanies camouflaged objects. In this paper, a Progressive Refinement Network called PRNet is proposed based on human perception of camouflaged images. Specifically, we develop a position-aware module to roughly locate the position of camouflaged objects by reverse-guiding with high-level semantic information. Moreover, an edge-guided fusion module is designed to simultaneously refine the boundaries and regions of camouflaged objects by using edge features as a guide in cross-level feature fusion. Benefited from the utility of the above two modules, our PRNet is able to identify camouflaged objects accurately and quickly. Numerous experiments on four widely used benchmark datasets demonstrate that the proposed PRNet is an efficient COD model, outperforming 14 state-of-the-art algorithms significantly and running at a real-time