Camouflaged object detection(COD)aims to identify objects that are highly similar to their surrounding environment.To address the issues of incomplete detection results and blurred edge details in current COD methods,a camouflaged object detection method that integrates context awareness and background exploration(CABENet)is proposed.Firstly,the model employs the Swin-Transformer as the backbone network to extract global context information at multiple scales.Secondly,it utilizes a proposed attention-based hierarchical context-aware module to enlarge the receptive field,enhancing feature extraction capabilities from both channel and spatial dimensions,followed by a fully connected decoder to capture coarse position maps of hidden objects.Lastly,by integrating an attention mechanism,the background exploration module explores edge clues from background information,enhancing the extraction of edge features of camouflaged objects.Experimental results on the CHAMELEON,CAMO,and COD10K datasets demonstrate that this method outperforms ten representative models on four evaluation metrics.On the COD10K dataset,the mean absolute error(MAE)is reduced to 0.026.