Abstract

Lane line recognition remains a crucial component of autonomous driving, particularly under complex scenarios involving illumination changes and occlusions. This paper presents a structurally efficient and robust improvement of the UFLD-V2 architecture, designed for real-time and reliable lane detection. The proposed method integrates three lightweight yet complementary components: (1) Res2Net, replacing the original ResNet backbone, enhances multi-scale feature extraction and inference efficiency through reparameterization; (2) an Efficient Multi-scale Attention (EMA) module captures fine-grained contextual details across varying scene complexities; and (3) the Simple Attention Module (SimAM) is applied in the segmentation head to suppress background noise and improve localization accuracy. Unlike prior work that uses these modules in isolation, we propose a tailored integration strategy that achieves a favorable trade-off between accuracy and computational cost. Extensive experiments on the TuSimple dataset show the effectiveness of our method, achieving 0.95947 accuracy, 0.0262 false positive rate, 0.02328 false negative rate, and an F1 score of 0.96517. Our approach surpasses several state-of-the-art models, including UFLD, PolyLaneNet, EL-GAN, SAD, CurveFormer++, BEVLaneDet, and PersFormer, particularly under challenging conditions. These findings highlight the potential of our approach for practical deployment in intelligent driving systems.