基于密集分割网络的车道线检测方法

计算机工程与科学

基于密集分割网络的车道线检测方法

丁海涛，孙锐，程旭升，高隽

（合肥工业大学计算机与信息学院，安徽合肥 230601）

收稿日期:2019-05-08 修回日期:2019-07-09 出版日期:2020-03-25 发布日期:2020-03-25
基金资助:
国家自然科学基金（61471154）;中央高校基本科研业务费专项资金（JZ2018YYPY0287）

A lane line detection method based

on dense segmentation network

DING Hai-tao，SUN Rui，CHENG Xu-sheng，GAO Jun

(School of Computer and Information,Hefei University of Technology，Hefei 230601,China)

Received:2019-05-08 Revised:2019-07-09 Online:2020-03-25 Published:2020-03-25

摘要/Abstract

摘要：

传统车道线检测算法大多数依赖手工制作特征和启发式算法的组合，容易受车辆遮挡和地面污损等因素的影响。针对影响车道线检测的复杂问题，将车道线检测视为连续细长区域实例分割问题，提出了一种基于密集分割网络的车道线检测方法。为此，使用稠密块构建了一个密集分割网络DSNet，该网络能够利用特征重复使用的特性提高提取车道线实例特征和恢复特征图分辨率的性能。同时，还引入了邻近AND运算和Meanshift聚类算法对DSNet网络的输出进行处理，减小了非车道线像素的影响，使得检测结果的边界线更为清晰。实验表明，本文方法能很好地解决车辆遮挡和地面污损问题，并且还能确定车道线的数量，具有较好的鲁棒性和实时性。

关键词: 智能交通, 车道线检测, 实例分割, DSNet, 邻近AND运算, Meanshift聚类

Abstract:

Most traditional lane detection algorithms rely on the combination of handcrafted features and heuristic algorithms, which are easily affected by factors such as vehicle occlusion and ground fouling. Aiming at the complicated problems that affect lane line detection, this paper considers lane line detection as a continuous segmentation problem, and proposes a lane line detection method based on dense segmentation network. To this end, dense blocks are used to construct a dense segmentation network (DSNet), so DSNet can reuse features to improve the performance of extracting lane line instance features and restoring the feature map resolution. At the same time, the proximity AND operation and Meanshift clustering algorithm are also introduced to process the output of DSNet, which reduces the influence of non-lane line pixels and makes the boundary of detection results more specific. Experiments show that the proposed algorithm can well solve the problem of vehicle occlusion and ground fouling, and can also determine the number of lane lines, which has better robustness and real-time performance.

Key words: intelligent transportation, lane line detection, instance segmentation, DSNet, proximity AND operation, Meanshift clustering

丁海涛，孙锐，程旭升，高隽. 基于密集分割网络的车道线检测方法[J]. 计算机工程与科学.

DING Hai-tao，SUN Rui，CHENG Xu-sheng，GAO Jun.

A lane line detection method based

on dense segmentation network

[J]. Computer Engineering & Science.

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