In this paper,a progressive network based on edge and attention (EAPNet) is proposed to address the issue of insufficient inter-layer information utilization in existing camouflage object detection (COD) models,which leads to blurred detection results.First,an edge information extraction module (EIEM) is proposed to extract features from the first,third,and fifth stages of the backbone network to generate edge feature maps;next,a receptive field and edge aggregation module (RFEAM) is proposed to aggregate receptive field features and edge features from adjacent stages of the backbone network;finally,the proposed attention-assisted fusion module (AAFM) is used to perform secondary progressive fusion on the output features of adjacent RFEAMs.Experimental results on three COD datasets show that the proposed method outperforms ten other models.