Camouflaged Object Detection with State-Space Model

Camouflaged object detection (COD) remains a challenging task in computer vision due to the intrinsic similarity between camouflaged objects and their backgrounds. Traditional methods often struggle with the complexities of camouflage. In this study, we propose a novel COD framework, SSM-COD, which employs a state-space model (SSM) to effectively distinguish camouflaged objects. Our approach leverages the dynamic properties of SSM to capture both global and local correlations, enabling robust detection in complex environments. We conducted comprehensive experiments on three widely used benchmark datasets: CODIOK, CAMO, and CHAMELEON. Rigorous testing and ablation studies demonstrate that SSMCOD significantly outperforms existing COD techniques in detection accuracy and reduces missed detections. These findings highlight the transformative potential of the state-space model in enhancing COD performance and indicate promising directions for future research aimed at improving the robustness and applicability of camouflaged object detection systems.