The goal of Camouflage Object Segmentation （COS） is to detect hidden objects from the background. In recent years， Camouflage Object Detection （COD） based on Convolutional Neural Network （CNN） has developed rapidly， but there is still a problem that the complete object cannot be accurately detected in scenes with highly similar foreground/background. For the above problem， a COS method based on Channel Attention （CA） and edge fusion， called CANet （Network based on Channel Attention and edge fusion）， was proposed to obtain a complete segmentation result with clearer edge details of camouflage objects. Firstly， the SE （Squeeze-and-Excitation） attention was introduced to extract richer high-level semantic features. Secondly， an edge fusion module was proposed to restrain interference in low-level features and make full use of edge details information of the image. Finally， a channel attention module based on depthwise separable convolution was designed to gradually integrate cross-level multi-scale features in a top-down manner， which further improved detection accuracy and efficiency. Experimental results on multiple public COD datasets show that compared to eight mainstream methods such as SINet （Search Identification Net）， TINet （Texture-aware Interactive guidance Network） and C²FNet （Context-aware Cross-level Fusion Network）， CANet performs better and can obtain rich camouflage objects’ internal and edge detail information. Among them， CANet improves the structure-measure index by 2.6 percentage points compared to SINet on the challenging COD10K dataset. CANet has superior performance and is suitable for medical detection of lesion areas similar to human tissue， military detection of hidden targets， and other related fields.