Road Segmentation for ADAS/AD Applications
Fuente:
arXiv
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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866913845107228672 |
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| author | Ramasamy, Mathanesh Vellingiri Kurniasalim, Dimas Rizky |
| author_facet | Ramasamy, Mathanesh Vellingiri Kurniasalim, Dimas Rizky |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_12206 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Road Segmentation for ADAS/AD Applications Ramasamy, Mathanesh Vellingiri Kurniasalim, Dimas Rizky Computer Vision and Pattern Recognition Machine Learning 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. |
| title | Road Segmentation for ADAS/AD Applications |
| topic | Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2505.12206 |