Abstract: Traditional object detection models assume that the training and test data come from the same or similar scenes, but the assumption is difficult to satisfy in practical applications, that is, detection models are often required to work in different environments or scenarios, which inevitably affects traditional models and leads to a significant decrease in detection accuracy. To address this issue, cross-domain object detection has received widespread attention in recent years. This survey presents the development history and relevant methods of cross-domain object detection in recent years, categorizing cross-domain object detection algorithms into three main classes: those based on transfer learning, self-learning, and image generation. Among these, transfer learning algorithms integrate domain adaptation and object detection algorithms to enhance the model's adaptability to different environments. Self-learning algorithms leverage pseudo-labels to improve the model's transferability on the target domain. Image generation-based algorithms utilize generative adversarial networks to generate relevant images for assisting model training, thereby enhancing the model's performance in the target domain. Additionally, relevant datasets used for cross-domain object detection and the performance of representative algorithms are introduced. Finally, the current classification of cross-domain object detection is summarized, and existing shortcomings are highlighted. Moreover, new directions for future research are indicated, including the exploration of generalization performance in unknown domains, the resolution of data privacy concerns, and the application of visual prompt techniques.