Abstract: Nighttime object detection presents significant challenges due to the low luminance of targets and the high cost of manually annotating large-scale nighttime datasets, making supervised training difficult. To address these issues, a domain adaptation method DTN-DETR for object detection tailored to nighttime imagery based on improved RT-DETR is proposed. First, a Photometric Consistency Matching is designed to generate a synthetic dataset resembling the nighttime domain by aligning the photometric properties of the daytime source domain with the nighttime target domain. Second, a backbone network improved Bidomain Refinement Module (BRM) is proposed, which comprises two key components: the Feature Refinement Module (FRM) and the Bidomain Information Interaction (BII) module. The FRM eliminates redundant information in the feature channels. The BII module leverages the interaction between the frequency and spatial domains to handle glare and noise with inconsistent frequency characteristics, addressing the coupling phenomena of multiple local light sources in nighttime scenes. Finally, a P2 detection head is introduced, which enhances the perception of small objects in nighttime scenes through multi-level feature fusion. Experimental results on the public datasets BDD100K, SODA10M and Foggy Cityscapes demonstrate that the proposed method significantly outperforms existing state-of-the-art approaches in object detection tasks, validating its effectiveness and robustness.

摘要： 夜间目标检测场景中，由于目标亮度较低且手动标注大规模夜间图像成本高昂，很难进行大规模的有监督训练。为解决这一问题，提出了一种基于改进RT-DETR的夜间图像域适应目标检测方法DTN-DETR。首先，设计了一种光度一致性匹配方法，将白天源域的光度特性与夜间目标域相匹配，生成类似目标域的夜间图像。其次，提出了双域优化模块改进的骨干网络，包含两个核心设计：特征优化模块和双域信息交互模块。特征优化消除特征通道中的冗余信息。双域信息交互则利用频率域和空间域信息交互，处理具有不一致频率的眩光和噪声，解决夜间场景局部多光源的耦合性现象。最后，引入了P2检测头，通过多层次特征融合提升夜间场景小目标的感知能力。在公共数据集BDD100K，SODA10M和Foggy Cityscapes上的实验结果表明，所提出的方法在目标检测任务中相较于现有的最先进方法具有显著的性能优势，验证了其有效性和鲁棒性。