Synthetic Data for 2D Road Marking Detection in Autonomous Driving

The development of autonomous driving generally requires enormous annotated data as training input. The availability and quality of annotated data have been major restrictions in industry. Data synthesis techniques are then being developed to generate annotated data. This paper proposes a 2D data synthesis pipeline using original background images and target templates to synthesize labeled data for model training in autonomous driving. The main steps include: acquiring templates from template libraries or alternative approaches, augmenting the obtained templates with diverse techniques, determining the positioning of templates in images, fusing templates with background images to synthesize data, and finally employing the synthetic data for subsequent detection and segmentation tasks. Specially, this paper synthesizes traffic data such as traffic signs, traffic lights, and ground arrow markings in 2D scenes based on the pipeline. The effectiveness of this pipeline was verified on the public TT100k dataset and the CeyMo dataset by image detection tasks. Template positioning methods including random location and same position replacement were employed for synthesis in traffic sign detection. For ground arrow marking detection, template positioning methods encompassing inverse perspective transformation and lane line positioning were utilized. Extensive experiments were carried out on the TT100K dataset and the CeyMo dataset. The performance between those open datasets and the synthetic data in this paper were then compared. The results show that the detection model trained entirely on synthetic data can achieve up to 86% mAP@0.5 on the TT100k dataset validation set, and choosing 50% of the CeyMo training set for fine-tuning can achieve 77% mAP@0.5. We have verified that data synthesis for categories with less data can effectively mitigate the class imbalance problem in datasets. This demonstrates that the pipeline proposed in this paper is a practical and effective approach in the field of autonomous driving data synthesis.