With the rapid development of urban ground transportation, lane line detection is gradually becoming a major technological direction to help to realize safe vehicle navigation. However, lane line detection results may have incompleteness issues, such as blurry lane lines and disappearance of the lane lines in the distance, since the lane lines may be heavily obscured by vehicles and pedestrians on the road. In addition, low-visibility environments also pose a challenge for lane line detection. To solve the above problems, we propose a dynamic data augmentation framework based on imitating real scenes (DDA-IRS). DDA-IRS contains three data augmentation strategies that simulate different realistic scenes (i.e., shadows, dazzle, and crowded). In this way, we expand from a limited scene dataset to realistically fit multiple complex scenes. Importantly, DDA-IRS is a lightweight framework that can be integrated with a variety of training-based models without modifying the original model. We evaluate the proposed DDA-IRS on the CULane dataset, and the results show that the data-enhanced model outperforms the baseline model by 0.5% in terms of F-measure. In particular, the F-measure of the “Normal”, “Crowded”, “Shadow”, “Arrow”, and “Curve” achieve a 0.4%, 0.1%, 1.6%, 0.4%, and 1.4% improvement, respectively.