Camouflage object detection (COD) aims to accurately and efficiently detect camouflaged objects that are highly similar to the background. Its method can assist in species protection, medical patient detection, and military monitoring, possessing high practical value. In recent years, using deep learning methods to detect camouflaged objects has become an emerging research direction. However, most existing COD algorithms apply a convolutional neural network (CNN) as the feature extraction network and ignore the influence of feature representation and fusion methods on detection performance when combining multi-level features. As the camouflage object detection model based on CNN has a weak ability to extract the global features of the detected object, this study proposes a cross scale interactive learning method for camouflage object detection based on Transformer. The model first puts forward a dual branch feature fusion module, which fuses features that have undergone iterative attention to better fuse high- and low-level features. Secondly, a multi-scale global context information module is introduced to fully integrate context information to enhance features. Finally, a multi-channel pooling module is proposed, which can focus on the local information of the detected object and improve the accuracy of camouflage target detection. The experimental results on the CHAMELEON, CAMO, and COD10K datasets show that this method generates clearer prediction maps and can achieve higher accuracy in camouflage object detection models than current mainstream camouflage object detection algorithms.