Abstract: To address the scarcity of labeled data in low-light object detection, an unsupervised domain-adaptive object detection framework is proposed. First, a pixel-level alignment module is employed for image translation, thereby reducing the distribution discrepancy between the source and target domains. Then, a multi-layer feature alignment module with noise-aware attention is designed to mitigate severe noise in low-light images, where an adversarial classifier and gradient reversal layer(GRL) are incorporated to further align global features across domains. Finally, an instance-level feature alignment module with global context is introduced to improve generalization by enhancing feature consistency in foreground regions. Experimental results show that the proposed method significantly improves detection performance in low-light images without requiring additional annotations or preprocessing.