Low-light condition significantly impair object detection performance due to severe image degradation. Existing methods fail to effectively reduce the impact of degradation, thus lacking clean detection-specific knowledge. To overcome these challenges, we propose a teacher-student model(TSM) with Clean Feature Distillation(CFD). Specifically, we design a Bridge Module(BM) to bridge the feature gap between effective image content reconstruction and detection, transferring pixel-level features to semantic-level detection features. Moreover, we propose Clean Feature Distillation (CFD) to distill degradation-free and clean features from a teacher model trained on clean images to a student model, thereby enhancing detection performance under low-light conditions. Finally, we introduce a more generalizable Low-light Image Degradation Synthesis Pipeline(LIDSP) to simulate a wide range of low-light degradation scenarios. Experimental results demonstrate that our approach outperforms existing methods, significantly boosting object detection performance under low-light conditions.