Single-domain generalization is essential for object detection, particularly when training models on a single source domain and evaluating them on unseen target domains. Domain shifts, such as changes in weather, lighting, or scene conditions, pose significant challenges to the generalization ability of existing models. To address this, we propose Cross-Domain Feature Knowledge Distillation (CD-FKD), which enhances the generalization capability of the student network by leveraging both global and instance-wise feature distillation. The proposed method uses diversified data through downscaling and corruption to train the student network, whereas the teacher network receives the original source domain data. The student network mimics the features of the teacher through both global and instance-wise distillation, enabling it to extract object-centric features effectively, even for objects that are difficult to detect owing to corruption. Extensive experiments on challenging scenes demonstrate that CD-FKD outperforms state-of-the-art methods in both target domain generalization and source domain performance, validating its effectiveness in improving object detection robustness to domain shifts. This approach is valuable in real-world applications, like autonomous driving and surveillance, where robust object detection in diverse environments is crucial.

翻译：单域泛化对于目标检测至关重要，尤其是在单一源域上训练模型并在未见过的目标域上评估时。诸如天气、光照或场景条件变化等域偏移对现有模型的泛化能力构成了重大挑战。为解决这一问题，我们提出了跨域特征知识蒸馏（CD-FKD），该方法通过利用全局特征蒸馏和实例级特征蒸馏来增强学生网络的泛化能力。所提出的方法通过降尺度和数据损坏生成多样化数据来训练学生网络，而教师网络则接收原始的源域数据。学生网络通过全局和实例级蒸馏来模仿教师的特征，使其能够有效提取以目标为中心的特征，即使对于因数据损坏而难以检测的目标也是如此。在具有挑战性的场景上进行的大量实验表明，CD-FKD在目标域泛化和源域性能方面均优于现有最先进方法，验证了其在提升目标检测对域偏移的鲁棒性方面的有效性。该方法在自动驾驶和监控等实际应用中具有重要价值，因为在多样环境中实现鲁棒的目标检测至关重要。