A Novel Strategy for Global Lane Detection Based on Key-Point Regression and Multi-Scale Feature Fusion

Lane detection is a fundamental task for autonomous driving. Most existed deep learning-based methods use a combination of semantic segmentation and post-processing for lane information extraction. Such methods not only tend to ignore global lane information but also bring the problem of low efficiency due to the complex models. To solve these problems, a novel global lane detection method based on key-point regression and multi-scale features fusion (KP-MFF) is proposed in this study. Firstly, a regression strategy is presented to generate key-point sequences in each grid of the image for locating the lane. Moreover, a multi-scale feature fusion module is proposed to merge feature maps of different scales. Additionally, a rule-based fast post-processing method is proposed to deal with the series of key-point sequences output by the CNN model, which further improves the lane detection accuracy. Experiments on CULane and TuSimple datasets demonstrate that the proposed method performs more effectively (417 FPS on NVIDIA 2080Ti and 91 FPS on NVIDIA Jetson AGX Xavier) while maintaining competitive accuracy compared with state-of-the-art methods. The road test also validates the practicability and effectiveness of the proposed method.