Advanced Camouflage Object Detection Strategy Integrating Frequency Domain Analysis

Camouflaged object detection is an important research topic in the field of computer vision. Owing to the high similarity between camouflaged objects and their surroundings in the spatial domain, target recognition becomes extremely difficult. Although various new methods have been proposed to address this issue, limitations in the spatial domain features and deficiencies in the application of frequency features still exist. To solve this problem, we propose a new method that utilizes the FAM module to enhance the ability to capture and distinguish target features while retaining significant features and effectively reducing the interference of irrelevant features, thereby overcoming the limitations of the spatial domain. Furthermore, to further improve the model performance, we used the FMDM module to enhance the model's feature extraction capability and improve the convergence efficiency and detection accuracy of the model. The experimental results show that our method outperforms current mainstream models in terms of accuracy in camouflaged object detection.