While deep learning-based approaches have notably advanced the accuracy of lane detection in recent years, they remain limited in multi-scale feature modeling and the capture of fine-grained details. Methods such as CLRerNet model, which integrate refinement strategies and intersection over union (IoU) based loss optimization, have delivered strong performance; however, they still exhibit shortcomings in suppressing false positives and negatives, effectively fusing features, and optimizing overall network architecture. To address these challenges and improve robustness in complex driving environments, we propose an enhanced framework built upon CLRerNet, called ECLRerNet. The model introduces the MaxECA module into the segmentation branch—an enhanced efficient channel attention mechanism to enable adaptive multi-scale feature fusion, thereby improving the extraction of subtle lane structures. Furthermore, a large-kernel grouped attention gate (LGAG) module is designed to alleviate information imbalance during partial multi-scale fusion within the feature pyramid network. By dynamically regulating feature flow through an attention-guided mechanism, LGAG module reduces feature conflicts and enhances robustness. In addition, an improved IoU, termed ELaneIoU, is integrated into the loss function to suppress both false positives and false negatives by reducing the influence of erroneous lane points on loss computation. Extensive experimental results on the CurveLanes and CULane datasets demonstrate that ECLRerNet model achieves substantial performance improvements, reaching an F1-score of 86.77% on CurveLanes and 81.58% on CULane—outperforming all existing open-source lane detection models on the CULane benchmark. These results verify the potential of ECLRerNet for reliable lane detection in real-world driving scenarios.
LI et al. (Mon,) studied this question.