A large amount of training data (including samples and labels) are required to achieve the task of object detection. However, the construction of databases generally has several problems. The samples are not balanced. In some scenarios, very few or even no samples can be collected; manual collection and labeling are expensive. Obtaining samples from public object detection data sets is simple, but the categories that can be obtained are limited and cannot meet the needs of other detection tasks. Therefore, we propose a method to automatically construct a flexible object detection database and complete automatic labeling. Our main advantage lies in the proposed synthetic region screening and adaptive feature fusion algorithm, which generates sample images and corresponding annotation files that are similar to the real collection. Our algorithm has a stronger and more realistic synthesis ability for three-dimensional objects than other data synthesis methods. Our method improves the system’s object detection accuracy in the following ways: (1) it synthesizes a wider range of images for certain categories with few samples in the data set to solve the problem of sample imbalance; (2) it adds new categories to the public data set to meet the needs of rapid deployment of special object detection tasks; and (3) it fuses different foregrounds and backgrounds to enrich the diversity of database samples. To effectively evaluate the proposed method, we conducted experiments separately, and the experimental results proved that our method is superior to other existing data synthesis and data enhancement methods. Combining the generated samples with the real data set, the object detection accuracy value increased from 9.47% to 58.92%. Extending the objects of the self-collected data set to the public data set and automatically generating high-quality annotations, the detection accuracy of extended objects reached more than 50%.