主办:陕西省汽车工程学会
ISSN 1671-7988  CN 61-1394/TH
创刊:1976年

汽车实用技术 ›› 2022, Vol. 48 ›› Issue (5): 34-39.DOI: 10.16638/j.cnki.1671-7988.2023.05.006

• 智能网联汽车 • 上一篇    

基于深度学习的快速车道线检测方法

刘 彬,魏为民   

  1. 上海电力大学 计算机科学与技术学院
  • 出版日期:2023-03-15 发布日期:2023-03-15
  • 通讯作者: 刘 彬
  • 作者简介:刘彬(1991—),男,硕士研究生,研究方向为计算机视觉、自动驾驶,E-mail:15221564185@sina.cn。

Fast Lane Detection Method Based on Deep Learning

LIU Bin, WEI Weimin   

  1. College of Computer Science and Technology, Shanghai University of Electric Power
  • Online:2023-03-15 Published:2023-03-15
  • Contact: LIU Bin

摘要: 针对目前基于深度学习的车道线检测方法普遍存在的实时性较差的问题,文章中提出 了一种高效的车道线检测方法 LaneBezierNet。该方法从前置摄像头获取图像后,先使用数据 增强技术对图像进行处理,然后通过贝塞尔曲线回归模型直接输出图像中每条车道线的贝塞 尔曲线控制点坐标,结合贝塞尔曲线方程便可以得到车道线上的每个坐标点信息。实验结果 表明,在 Tusimple 公开数据集上达到了 92.89%的较高准确率的同时,每秒传输帧数(FPS)达 到了 116 bits/s。相较于基于图像分割的车道线检测方法,该方法在检测速度上有着明显提升。 该算法在检测准确率未明显下降的前提下极大地提升了检测效率,更加符合实际项目需求。

关键词: 车道线检测;EfficientNet;残差网络;贝塞尔曲线

Abstract: Aiming at the problem of poor real-time nature of the current lane detection method based on deep learning, an efficient lane detection method LaneBezierNet is proposed in this paper. After acquiring the image from the front camera, firstly the method uses data enhancement technology to process the image, then directly outputs the Bezier curve control point coordinates of each lane in the image through the Bezier curve regression model, and the information of each coordinate point on the lane can be obtained by combining the Bezier curve equation. Experimental results show that while the higher accuracy rate of 92.89% on the Tusimple public dataset is achieved, the frames per second (FPS) reaches 116 bits/s. Compared with the lane detection method based on image segmentation, this method has a significant improvement in detection speed. The algorithm greatly improves the detection efficiency without significantly reducing the detection accuracy, which is more in line with the actual project requirements.

Key words: Lane detection; EfficientNet; Residual networks; Bezier curv