Abstract: Lane detection technology plays a crucial role in autonomous driving systems. Currently, deep learning-based methods for lane detection typically involve extracting features from a backbone network, followed by confidence estimation of key points on the lane lines and their offsets relative to a starting point. However, existing backbone networks struggle to effectively capture features of elongated lanes, and offset networks face challenges in regressing the offsets of key points along the lane line. In this paper, we propose a hybrid network model called CTNet (CNN-Transformer hybrid network) based on a pointbased lane detection approach. CTNet enhances feature representation through a feature pyramid network and an augmented coordinate attention mechanism. Additionally, it employs a vision transformer-based offset network to regress crucial offsets. Consequently, CTNet extracts elongated lane line features, captures long-range offsets between points, and significantly improves the accuracy of lane detection. Experiments conducted on the TuSimple and CULane datasets demonstrate that CTNet outperforms six commonly used lane detection algorithms across various accuracy metrics. Specifically, CTNet achieves superior results on TuSimple across all evaluation metrics. Furthermore, when tested across nine different lane scenarios in the CULane dataset, CTNet achieves the highest accuracy in six scenarios.

Key words: lane line detection, visual Transformer, coordinate attention(CA), feature pyramid network (FPN)