Abstract: The task of camouflaged object detection is to identify objects that blend into the background due to similar features such as color and texture. However, current research on camouflaged target detection has not fully considered the impact of edge features on detection performance, and there are still issues such as missed detection and incorrect classification, thus the detection accuracy needs further improvement. To overcome the above shortcomings, this paper proposes a camouflaged object detection method based on intra-layer dual-branch mutual enhancement attention, which introduces an object edge prediction module into the existing multi-supervision mechanism to further boost detection performance of the model. To enhance the model’s spatial localization and recognition capabilities for objects, with the Swin Transformer model as the backbone, a novel intra-layer dual-branch mutual enhancement attention module composed of a dual attention enhancement module and a simple mutual enhancement module is developed. Comprehensive experiments were conduct to evaluate the performance of the model on three mainstream benchmark datasets, including CAMO, COD10K, and NC4K, comparing which with 18 typical lgorithms existing. Experimental results show that the presented model has superior performance, significantly outperforming current eighteen state-of-the-art methods in terms of four evaluation indexes, including S_\alpha , \alpha E, \omega F, \mathrmMAE .