With the prosperous development of deep learning, people pay more attention to the needs of different training data. In
this paper, we propose a method to automatically generate realistic training data for industrial components detection. Our
method can generate a large scale of various synthetic images associated with the corresponding precise instance segmentation masks through the concept of domain randomization and style transfer. Besides, we demonstrate that the proposed method is effective to generate images for training a wrench detector. Our method can enhance the performance of the wrench detection task obviously, and it can be easily extended to the detection of different kinds of industrial components. As far as we know, the proposed method is novel and effective for generating training data for training detectors for industrial components.