Camouflaged Object Detection for Aerial Images Based on Bidirectional Cross-attentionCross-domain Fusion

To address the challenges of highly integration with the environment and the high demand for real-time performance,this paper proposes a camouflaged object detection model for aerial images using bidirectional cross-attention cross-domain fusion.Firstly,a feature extraction network with two branches is constructed to extract features from both RGB and frequency domain.Simultaneously,frequency features and RGB features are crossly fused at multiple scales using bidirectional cross-attention fusion modules,effectively improving the network’s representational capacity.Experimental results show that the proposed model achieves a better balance between target recognition accuracy and real-time performance,compared to other representative models.