基于改进Enet网络的车道线检测算法

Lane Detection Algorithm Based on Improved Enet Network

针对实际驾驶环境中道路场景及车道线复杂多样的问题,提出一种基于改进Enet网络的车道线检测算法。首先,对Enet网络进行剪枝和卷积优化操作,并利用改进的Enet网络对车道线进行像素级图像语义分割,将车道线从图像中分离出来。然后,采用DBSCAN算法对分割结果进行聚类处理,将相邻车道线区分开来。最后,对车道线聚类结果进行自适应拟合,得到最终的车道线检测结果。该算法在香港中文大学的CULane数据集上进行了训练和测试,结果表明,其标准路面检测准确率达到96.3%,各种路面综合检测准确率为78.9%,图像帧处理速度为71.4fps,能够满足实际驾驶环境中的复杂路况和实时性需求。此外,该算法还在图森未来的TuSimple数据集和实采数据集LD-Data上进行了训练和测试,均取得了实时性的检测结果。

Aiming at the complex diversity of road scenes and lane lines in the actual driving environment,a lane-line detection algorithm based on improved Enet network was proposed.Firstly,the Enet network is pruned and convolution optimized.The improved Enet network is used to segment the lane-line image semantics and separate the lane lines from the image.Then,the DBSCAN algorithm is used to cluster the segmentation results to distinguish adjacent lane lines from each other.Finally,the lane line clustering results are adaptively fitted to obtain the final lane line detection results.The proposed algorithm was trained and tested in the CULane dataset of the Chinese University of Hong Kong.The accuracy of standard pavement detection is 96.3%,the accuracy of comprehensive pavement detection is 78.9%,and the image frame processing speed is 71.4fps,which can meet the complex road conditions and real-time requirements in actual driving environment.In addition,the proposed algorithm has been trained and tested on Tucson’s future TuSimple dataset and our actual acquisition dataset LD-Data,all of which have achieved real time detection results.