Depth-aware framework for small-scale camouflaged object detection via swin transformer and ghost convolution layer integration

This study introduces a depth-aware approach for detecting small-scale camouflaged objects, leveraging the Swin Transformer and Ghost Convolution Layer. We employ multimodal depth maps to enhance spatial understanding, which is crucial for identifying camouflaged items. The Swin Transformer captures extensive contextual data, while the Ghost Convolution Layer boosts computational efficiency. We validate our method on unique quasi-synthetic and comparative synthetic datasets created for this study. An ablation study and GRAD-CAM visualization further substantiate the model's effectiveness. This research offers a novel framework for improving object detection in challenging camouflaged environments.