DAS-COD: Depth-Aware Camouflaged Object Detection via Swin Transformer

The successful integration of depth information into salient object detection tasks has catalyzed research interest in depth-enhanced camouflaged object detection (COD) tasks. However, the challenges associated with acquiring depth information pose significant hurdles to this task, especially given the lack of RGB-D datasets tailored specifically for COD. Consequently, employing depth estimation techniques to generate pseudo-depth information emerges as a viable solution in the realm of depth-enhanced COD tasks. In this study, we propose an architecture, DAS-COD (Depth-Aware Swin Transformer COD), that integrates Swin Transformer model with depth estimation techniques for the purpose of camouflaged object detection. In particular, we use a dual-stream Swin Transformer backbone to extract feature maps from different modalities. These maps are then enhanced with a multi-modal feature enhancement module. Additionally, to address the inherent discrepancies between pseudo-depth maps and actual depth information, we incorporate an edge-aware module to significantly improve the accuracy of boundary delineation in the predicted outcomes. We tested our proposed method on three different COD datasets. Our results show that the model achieves state-of-the-art performance across these camouflaged object detection datasets.