A challenge to apply deep learning-based computer vision technologies for assembly quality inspection lies in the diverse assembly approaches and the restricted annotated training data. This paper describes a method for overcoming the challenge by training an unsupervised domain adaptive object detection model on annotated synthetic images generated from CAD models and unannotated images captured from cameras. On a case study of pedal car front-wheel assembly, the model achieves promising results compared to other state-of-the-art object detection methods. Besides, the method is efficient to implement in production as it does not require manually annotated data.