A Comparative Study of Multi-Model Lane Detection Methods Based on a Unified Evaluation Framework

Shaopu Zou

2025

Abstract

Lane detection, as a crucial task in autonomous driving systems, faces the dual challenges of robustness and accuracy in complex road environments. This study conducts a comparative analysis of three representative deep learning models—Spatial Convolutional Neural Network (SCNN), Point Instance Network (PINet), and LaneATT. The models are reproduced and evaluated under consistent input settings using the unified CUHK Lane Dataset (CULane) and a standardized evaluation tool. Both quantitative metrics and visualized results are utilized to assess each model’s detection performance across diverse driving scenarios. Experimental results demonstrate that LaneATT achieves the best overall performance, particularly exhibiting strong robustness in challenging conditions such as nighttime and shadowed environments. PINet excels in curved lane detection, while SCNN maintains stable outputs in standard road settings. This study establishes a unified evaluation framework for horizontal comparisons of lane detection models, providing a systematic basis for performance assessment under standardized conditions. The proposed framework contributes to the advancement of algorithmic benchmarking and offers methodological guidance for subsequent research on model optimization and real-world deployment.

Paper Citation

in Harvard Style

Zou S. (2025). A Comparative Study of Multi-Model Lane Detection Methods Based on a Unified Evaluation Framework. In Proceedings of the 2nd International Conference on Engineering Management, Information Technology and Intelligence - Volume 1: EMITI; ISBN 978-989-758-792-4, SciTePress, pages 123-129. DOI: 10.5220/0014322600004718

in Bibtex Style

@conference{emiti25,
author={Shaopu Zou},
title={A Comparative Study of Multi-Model Lane Detection Methods Based on a Unified Evaluation Framework},
booktitle={Proceedings of the 2nd International Conference on Engineering Management, Information Technology and Intelligence - Volume 1: EMITI},
year={2025},
pages={123-129},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0014322600004718},
isbn={978-989-758-792-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 2nd International Conference on Engineering Management, Information Technology and Intelligence - Volume 1: EMITI
TI - A Comparative Study of Multi-Model Lane Detection Methods Based on a Unified Evaluation Framework
SN - 978-989-758-792-4
AU - Zou S.
PY - 2025
SP - 123
EP - 129
DO - 10.5220/0014322600004718
PB - SciTePress