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This invention discloses a lane line annotation method. First, it acquires the point and surface results of lane line detection separately. Then, it fuses the point and surface results using a multi-model approach to obtain a fused point set. Next, it tracks the fused point set and selects consecutive frames for tracking and fusion to obtain a merged point set. Finally, it performs fitting based on the merged point set and uses the fitting result as the lane line annotation result. This method automates lane line annotation and significantly improves its accuracy. Furthermore, it can quickly and accurately generate a large amount of sample data, providing a training set for lane line detection models and increasing the efficiency of developing new lane line detection models.