用AI生成数据增强，提升车道线检测在不同视角下的鲁棒性
Data Augmentation Strategies for Robust Lane Marking Detection
- 结合几何变换、AI补全和车辆遮挡，模拟真实部署视角
- 在SCNN和UFLDv2上提升精度、召回率与F1值
- 适合自动驾驶车道检测的实际部署场景
稳健的车道线检测对高级驾驶辅助和自动驾驶至关重要，但基于公开数据集（如CULane）训练的模型在不同摄像头视角下泛化能力差。本文针对侧装摄像头在车道-轮毂监测中的域偏移问题，提出一种基于生成式AI的数据增强流程。该方法融合几何透视变换、AI驱动的图像修补及车辆车身叠加，模拟部署特定视角的同时保持车道连续性。我们在两种先进模型SCNN和UFLDv2上评估了该增强策略的有效性。使用增强数据训练后，两个模型在阴影等复杂条件下均表现出更强的鲁棒性，实验结果显示精度、召回率和F1分数均优于预训练模型。该方法有效弥合了通用数据集与实际部署场景间的差距，为试点部署提供了可扩展且实用的车道检测可靠性提升框架。
原文摘要 · Abstract (English)
Robust lane detection is essential for advanced driver assistance and autonomous driving, yet models trained on public datasets such as CULane often fail to generalise across different camera viewpoints. This paper addresses the challenge of domain shift for side-mounted cameras used in lane-wheel monitoring by introducing a generative AI-based data enhancement pipeline. The approach combines geometric perspective transformation, AI-driven inpainting, and vehicle body overlays to simulate deployment-specific viewpoints while preserving lane continuity. We evaluated the effectiveness of the proposed augmentation in two state-of-the-art models, SCNN and UFLDv2. With the augmented data trained, both models show improved robustness to different conditions, including shadows. The experimental results demonstrate gains in precision, recall, and F1 score compared to the pre-trained model. By bridging the gap between widely available datasets and deployment-specific scenarios, our method provides a scalable and practical framework to improve the reliability of lane detection in a pilot deployment scenario.
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