Camouflaged object detection is focused on segmenting objects concealed within their surroundings. This technology can be applied in various fields such as medical image analysis, wildlife conservation, autonomous driving, and others. Existing semi-supervised camouflage object detection methods often suffer from poor network performance due to the accumulation of incorrect pseudo labels, and they fail to fully utilize multi-scale features or account for the diverse scale contexts necessary for various sizes of camouflage objects. In this paper, we propose an innovative semi-supervised learning strategy. We employ a dual-branch network named CAMNet, utilizing salient maps corresponding to camouflage objects to aid detection. We also introduce a Multi-Information Fusion Feature Perception module (MIF) and an Adaptive Receptive Field Selection module (ARFS), which are integrated into the network. Ultimately, we perform thorough comparative experiments on the R2C7K, COD-Water, and COD-Jungle datasets, showcasing superior performance in contrast to current state-of-the-art methods. We also conduct ablation experiments, further confirming the effectiveness of the proposed modules.