Abstract: Motorcycle driving is more dangerous than other driving styles but lacks effective driving assistance systems,such as lane assist systems,obstacle detection,pre-collision system,etc.The position of the lane line when driving is often used for determining whether the motorcycle has deviated.Therefore,lane line detection is very important for developing assisted driving systems,so this paper proposes a real-time detection algorithm for motorcycle lanes based on deep learning.This paper proposes three improvements based on the Lanenet architecture:1) using the absolute position of the lane coordinates as the input feature;2)using the K-means algorithm instead of the Mean-Shift algorithm;3) removing the H-net structure.Due to the lack of public motorcycle lane data sets,the collected motorcycle lane data will be used to fit the model in this paper.Experimental results prove the effectiveness of the proposed algorithm.The detection speed can reach 47.6fps,and the cross-combination ratio can reach 0.71560.Compared with the algorithm in reference [3],the accuracy improves by 15.5% and the speed improves by 53.3%.

Key words: Motorcycle driving, Lane detection, Lanenet, Real-time detection, Deep learning