TransAUAV: A Transformer-Enhanced RGB-Infrared Fusion Network for Anti-UAV Detection

To improve the accuracy and robustness of anti-UAV detection, this study introduces TransAUAV, a Transformer-based RGB- infrared image fusion detection network. This approach strengthens object feature representation by leveraging multi-modal data fusion and self-attention mechanisms. Specifically, 1) we propose a multi-modal attention fusion module, which enhances the complementarity of RGB and infrared image features through a dual-path attention mechanism; 2) we propose a cross-layer multi-scale Transformer module, which improves detection performance by extracting multi-scale features and facilitating cross-modal information interaction; 3) we propose a texture information focus module, which enhances the representation of local texture details. Additionally, this paper designs a hybrid loss function to improve feature discrimination capability and training efficiency. Experimental results on anti-UAV and Drone-detection datasets show that TransAUAV outperforms state-of-the-art methods on all evaluation metrics.