DDEF-Net: A Difference-Guided Detail Enhancement Fusion Network for UAV-Based RGB-T Object Detection
Yujie Li, Zhengsheng Chen, Decao Ma, Junjie Xu · Remote Sensing · 2026
This paper proposes a Difference-guided Detail Enhancement Fusion Network (DDEF-Net) for UAV-based RGB–thermal (RGB-T) object detection, which enables effective complementary exploitation of visible and infrared information in complex scenarios. A Difference-guided Kolmogorov–Arnold Network (KAN) Calibration Fusion module (DKCF) is designed to explicitly model cross-modal discrepancies and incorporate KAN-based nonlinear calibration, improving the selection of informative features and reducing redundant feature interference during multimodal fusion. Furthermore, a Scharr–Fourier Detail Enhancement module (SFDE) is introduced to jointly leverage Scharr edge priors and Fourier-domain information to strengthen low-level visible feature representations and preserve fine-grained structural cues. On the DroneVehicle dataset, DDEF-Net achieves 84.9% [email protected] and 72.6% [email protected]:0.95, improving the RGB–IR baseline by 3.5 and 4.0 percentage points, respectively, with 4.4 M parameters and 12.5 GFLOPs. An additional experiment on the VEDAI visible–near-infrared (NIR) aerial dataset after dataset-specific training provides supplementary evidence that the proposed modules remain beneficial under a different paired multimodal imaging setting. Corruption experiments show improved robustness to Gaussian and motion blur, whereas the model remains sensitive to strong Gaussian noise.