As target detection tends to be intelligent, the detection process at this stage needs to face the needs of different fields, in order to meet this demand has given rise to the technology of cross-domain target detection, this paper firstly briefly introduces the target detection technology of deep learning and introduces the cross-domain target detection technology, then according to the classification of cross-domain target detection methods, combined with the literature of the predecessors as well as the experiments, to summarize the methods used and the shortcomings. Subsequently, this paper lists the main cross-domain datasets used at this stage, including CDTD, Cityscapes and Foggy Cityscapes, M3D, as well as the commonly used evaluation metrics for cross-domain target detection, such as mAP, F-scatter, and H-scatter, which can accurately evaluate the effectiveness of the algorithms for cross-domain target detection, and finally, this paper lists the existing challenges of cross-domain target detection, which include domain differences, small sample learning and data enhancement and feature alignment, and looks forward in three directions, namely, improving adaptivity, enhancing robustness and strengthening portability, to provide a comprehensive reference framework for research and practice in the field of cross-domain target detection.