In the field of object detection, it is hoped that the model trained in the domain with a lot of labels can be applied to other domains without labels, but different domain distributions are always different to each other, such difference will result in a sharp decline of model performance in domain transfer. To improve the model performance of object detection in domain transfer, the domain transfer was addressed on two levels, including the global-level transfer and the local-level transfer, which were corresponding to different feature alignment methods, that is, the global-level adopted selective alignment and the local-level adopted full alignment. The proposed domain transfer framework was constructed based on Single Shot MultiBox Detector (SSD) model and was disposed of two domain adaptors corresponding to global and local level respectively for the purpose of alleviating the domain difference. The specific training was implemented by the adversarial network algorithm, and the consistency regularization was used to further improve the domain transfer performance of the model. The effectiveness of the proposed domain transfer model was verified by many experiments. Experimental results show that on various datasets, the proposed model outperforms the existing common domain transfer models such as Domain Adaptation-Faster Region-based Convolutional Neural Network(DA-FRCNN), Adversarial Discriminative Domain Adaptation (ADDA), Dynamic Adversarial Adaptation Network (DAAN) by 5%-10% in term of mean Average Precision (mAP).