对比VGG-16与U-Net在道路分割中的表现，验证模型架构与数据集的影响。
Road Segmentation for ADAS/AD Applications
- 修改VGG-16和U-Net分别在Comma10k与KITTI Road上训练
- 跨数据集测试中VGG-16的F1-score优于U-Net
- 结果对自动驾驶感知系统设计有参考价值
准确的道路分割对自动驾驶和高级驾驶辅助系统至关重要，有助于在复杂环境中实现有效导航。本研究通过在Comma10k数据集上训练改进的VGG-16，以及在KITTI Road数据集上训练改进的U-Net，分析模型架构与数据集选择对分割性能的影响。两个模型均取得高精度，跨数据集测试显示，尽管U-Net训练了更多轮次，其性能仍不及在另一数据集上训练的VGG-16。通过F1-score、平均交并比（mIoU）和精确率等指标评估模型表现，讨论了架构与数据集对结果的影响。
原文摘要 · Abstract (English)
Accurate road segmentation is essential for autonomous driving and ADAS, enabling effective navigation in complex environments. This study examines how model architecture and dataset choice affect segmentation by training a modified VGG-16 on the Comma10k dataset and a modified U-Net on the KITTI Road dataset. Both models achieved high accuracy, with cross-dataset testing showing VGG-16 outperforming U-Net despite U-Net being trained for more epochs. We analyze model performance using metrics such as F1-score, mean intersection over union, and precision, discussing how architecture and dataset impact results.
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