AT-DETR: A Multispectral Detector with Adaptive Feature Fusion and Scene Enhancement for Complex Road Scenes

In complex road environments, fog and low-light conditions degrade visible image quality, limiting detection systems. The integration of infrared (IR) imaging in these scenarios offers superior penetration capabilities and serves as a valuable complement to visible light. However, existing muiltispectral detectors struggle with modality imbalance and insufficient feature complementarity. To address these issues, a novel multispectral detection framework AT-DETR is proposed in this paper, with a two-stage training strategy and two essential modules: the adaptive feature alignment and fusion (AFAF) module and the three-branch dynamic parameter enhancement (TDPE) module. The AFAF module adaptively assigns fusion weights to balance the contribution of different modalities, while the TDPE module enhances the quality of degraded images through lightweight convolutional parameter estimation. The two-stage training strategy allows the network to initially prioritize feature extraction and subsequently optimize scene quality. The experimental results demonstrate that AT-DETR can improve mAP@0.5 by 7.1/8.6 on the combined dataset of the $\mathrm{M}^{3} \text{FD}$ and MSRS respectively, compared with using a single-source detector. Furthermore, its strong generalization capabilities are validated through tests on the FLIR_Aligned dataset. Implementation code and pre-trained weights can be accessed at https://github.com/JianDaoCX/ATDETR.