Unsupervised domain adaptation for object detection aims to bridge the domain gap by transferring knowledge from a labeled source domain to an unlabeled target domain, thus improving the performance of detection models. Common strategies focus on aligning the feature distributions between source and target domains to reduce their discrepancies. However, achieving complete alignment is often not feasible in real-world situations due to a lack of annotations in the target domain. Recently, TeacherStudent approaches achieve feature alignment by generating reliable target pseudo-labels and become the dominant solution for addressing this issue. However, due to the domain shift, the teacher model bias to source domain, making it challenging to enhance the quality of target pseudo-labels. Some methods within this framework attempt to overcome the domain shift by incorporating distribution alignment components, yet these approaches also face challenges in achieving perfect alignment between domains. In this paper, we propose the Dual-Domain Teacher (DDT) method to address the domain adaptation detection problem by simultaneously detecting objects in both domains, thereby decreasing the need for perfect alignment. To address the issue of duplicate detection results produced by the Dual-Domain detection process, a candidate set refinement strategy is proposed to eliminate these duplicates across domains. Moreover, when teachers generate pseudo-labels by selecting reliable predictions with fixed confidence thresholds, valuable predictions may be overlooked in mutual learning. In our approach, a minimum variance-based dynamic threshold module is designed to mine valuable pseudo-labels by adaptively adjusting to the optimal threshold. Extensive experiments show that the DDT achieve a 56.7$\%$mAP on the CityScapes-to-Foggy CityScapes task, marking a 4.8 point improvement over the latest methods. On the PASCAL VOC-to-Clipart1k task, our method reaches 51.2$\%$mAP, outperforming previous state-of-the-art.