基于语义分割的车辆行驶车道定位方法
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摘要
提出一种基于语义分割的车辆行驶车道定位方法.首先采用"编码器-解码器"网络架构实现多车道语义分割,通过最大池化计算的池化索引来进行非线性上采样,消除上采样的学习需要;然后结合目标检测YOLO v2算法,判断行驶车辆所属车道的位置,从而进行车道定位.利用卡尔斯鲁厄理工学院和丰田美国技术研究院公布的数据集(KITTI)中城市道路(UM)的数据制作训练和测试数据库,并将其公开发布.该算法可以实现端到端训练,网络结构简单、速度快、内存需求低,每帧图像的执行速度在60 ms以内.
A vehicle driving lane positioning method was proposed based on semantic segmentation. Firstly, an "encoder-decoder" network architecture was designed to implement multi-lane semantic segmentation. The decoder uses the pooled index of the largest pooled calculation in the corresponding encoder to perform nonlinear upsampling, eliminating the need for upsampling learning. Then, combine with the target detection YOLO v2 algorithm, and determine the location of the lane to which the vehicle belongs, further realize lane positioning. This project produced a train and test database using urban marked(UM) data from the Karlsruhe Institute of Technology and Toyota dataset(KITTI) and published it. The lane detection algorithm is fast and simple, running at 60 ms each frame, which can be trained end-to-end.
A vehicle driving lane positioning method was proposed based on semantic segmentation. Firstly, an "encoder-decoder" network architecture was designed to implement multi-lane semantic segmentation. The decoder uses the pooled index of the largest pooled calculation in the corresponding encoder to perform nonlinear upsampling, eliminating the need for upsampling learning. Then, combine with the target detection YOLO v2 algorithm, and determine the location of the lane to which the vehicle belongs, further realize lane positioning. This project produced a train and test database using urban marked(UM) data from the Karlsruhe Institute of Technology and Toyota dataset(KITTI) and published it. The lane detection algorithm is fast and simple, running at 60 ms each frame, which can be trained end-to-end.
引文
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[3] DUAN J,ZHANG Y,ZHENG B.Lane line recognition algorithm based on threshold segmentation and continuity of lane line[C]//IEEE International Conference on Computer and Communications.Chengdu:IEEE Press,2017:680-684.
[4] HAN S J,HAN Y J,HAHNH S.Lane and curvature detection algorithm based on the curve template matching method using top view image[J].Journal of the Institute of Electronics Engineers of Korea SP,2010,47(6):97-106.
[5] 肖晓明,马智,蔡自兴,等.一种自适应的区域生长算法用于道路分割[J].控制工程,2011,18(3):364-368.
[6] 郝运河,张浩峰,於敏杰,等.基于 K-means 特征的复杂环境下道路识别算法[J].计算机应用研究,2016,33(2):602-606.
[7] 王海,蔡英凤,贾允毅,等.基于深度卷积神经网络的场景自适应道路分割算法[J].电子与信息学报,2017,39(2):263-269.
[8] GIUSTI A,CIRESAN D C,MASCI J,et al.Fast image scanning with deep max-pooling convolutional neural networks[C]// 20th IEEE International Conference on Image Processing (ICIP).Melbourne:IEEE Press,2013:4034-4038.
[9] LONG J,SHELLHAMER E,DARRELL T.Fully convolutional networks for semantic segmentation[C]//IEEE Conference on Computer Vision and Pattern Recognition.Boston:IEEE Computer Society,2015:3431-3440.
[10] NOH H,HONG S,HAN B.Learning deconvolution network for semantic segmentation[C]//IEEE International Conference on Computer Vision.Santiago:IEEE Computer Society,2015:1520-1528.
[11] BADRINARAYANAN V,KENDALL A,CIPOLLA R.SegNet:a deep convolutional encoder-decoder architecture for image segmentation[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2017,39(12):2481-2495.DOI:10.1109/TPAMI.2016.2644615.
[12] PAPANDREOU G,CHEN L C,MURPHY K P,et al.Weakly-and semi-supervised learning of a deep convolutional network for semantic image segmentation[C]//IEEE International Conference on Computer Vision.Santiago:IEEE Computer Society,2015:1742-1750.
[13] ZHENG S,JAYASUMANA S,ROMERA-PAREDES B,et al.Conditional random fields as recurrent neural networks[C]// IEEE International Conference on Computer Vision.Santiago:IEEE Computer Society,2015:1529-1537.
[14] CHEN L C,PAPANDREOU G,KOKKINOS I,et al.Deeplab:semantic image segmentation with deep convolutional nets,atrous convolution,and fully connected CRFs[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2018,40(4):834-848.
[15] GIRSHICK R,DONAHUE J,DARRELL T,et al.Rich feature hierarchies for accurate object detection and semantic segmentation[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.Columbus:IEEE Computer Society,2014:580-587.
[16] GIRSHICKR.Fast R-CNN[C]//IEEE International Conference on Computer Vision.Santiago:IEEE Computer Society,2015:1440-1448.
[17] REN S,HE K,GIRSHICK R,et al.Faster R-CNN:towards real-time object detection with region proposal networks[J].IEEE Transactions on Pattern Analysis & Machine Intelligence,2017,39(6):1137-1149.
[18] REDMON J,FARHADI A.YOLO 9000:better,faster,stronger[C]//IEEE Conference on Computer Vision and Pattern Recognition.Honolulu:IEEE,2017:6517-6525.
[19] IOFFE S,SZEGEDY C.Batch normalization:accelerating deep network training by reducing internal covariate shift[C]// International Conference on International Conference on Machine Learning.Lille:JMLR Org,2015:448-456.
[20] LIU W,ANGUELOV D,ERHAN D,et al.Ssd:single shot multibox detector[C]//European Conference on Computer Vision.Amsterdam:Springer,Cham,2016:21-37.