Abstract:Cross-domain object detection is a new research direction, which aims to solve the problem of generalization from training set to test set. In the existing methods, using image style transfer and train the model on the converted data set is an effective method. However, this method has the problems of not end-to-end training, low efficiency, and tedious process. Therefore, we propose a new cross domain target detection algorithm based on image style migration, which can combine image style migration and target detection to carry out end-to-end training, and greatly simplify the training process. The results on several common datasets show the validity of the model.