天津师范大学学报（自然科学版）2025，Vol.45Issue(6)：1-8,12,9.DOI:10.19638/j.issn1671-1114.20250601
基于计算机视觉的车道线检测方法研究进展
Research progress of lane line detection methods based on computer vision
摘要
Abstract
Lane line detection is one of the key technologies in automatic driving and advanced driving assistance system,and can significantly improve road safety and reduce the incidence of traffic accidents.The latest research of 2D lane line de-tection technology based on computer vision is systematically analyzed and summarized.These research methods can be clas-sified into two categories:traditional algorithm-based and deep learning-based methods.Due to the poor performance of the traditional algorithm-based methods,the lane line detection methods based on deep learning are mainly analyzed and com-pared,and the deep learning-based methods are classified into four categories:segmentation-based,detection-based,key-point-based,and parameter curve-based.After summarizing the evaluation metrics and commonly used datasets,the perfor-mance of deep learning-based lane line detection methods is analyzed and compared.Finally,the future research directions in this field are prospected.关键词
车道线检测/深度学习/机器学习/图像分割/卷积神经网络Key words
lane line detection/deep learning/machine learning/image segmentation/convolutional neural networks分类
信息技术与安全科学引用本文复制引用
王淑琴,李兆发,景悦洲,张永琦,郭光远,林雨濛,陈明明..基于计算机视觉的车道线检测方法研究进展[J].天津师范大学学报（自然科学版）,2025,45(6):1-8,12,9.基金项目
国家自然科学基金资助项目(61070089) （61070089）
天津市应用基础与前沿技术研究计划重点资助项目(15JCYBJC4600) （15JCYBJC4600）
天津市科技计划资助项目(19JCZDJC35100) （19JCZDJC35100）
天津市教委科研计划资助项目(2021KJ187). （2021KJ187）
