Camouflaged object detection (COD) aims to detect camouflaged objects hidden in complex backgrounds. Due to the characteristics of camouflaged object, such as similar foreground and background textures and low-contrast edges, existing methods often produce blurry edge of prediction image and miss small object regions. Therefore, this paper proposes an edge information guided network (EIGNet). First, the edge of the camouflaged object is explicitly modeled through low-level and high-level features, which fully extract the edge features of the object to guide subsequent feature representations. Then, a dual-branch structure is used to process different dimensions of camouflaged object. The global branch is used to extract global contextual information to emphasize the global contribution of large objects, while the local branch is used to mine rich local low-level clues to enhance the feature representation of small objects. Finally, a top-down manner is used to gradually aggregate adjacent layer features to obtain a prediction image with fine edges and complete regions. Experimental results on three camouflaged datasets show that our method outperforms 15 other models, with a mean absolute error (MAE) of 0.044 on the NC4K dataset.