Instance segmentation based lane line detection and adaptive fitting algorithm
TIAN Jin1, YUAN Jiazheng2, LIU Hongzhe1
1. Beijing Key Laboratory of Information Service Engineering(Beijing Union University), Beijing 100101, China; 2. Institute of Intelligent Education, Beijing Open University, Beijing 100081, China
Abstract：Lane line detection is an important part of intelligent driving system. The traditional lane line detection method relies heavily on manual selection of features, which requires a large amount of work and has low accuracy when it is interfered by complex scenes such as object occlusion, illumination change and road abrasion. Therefore, designing a robust detection algorithm faces a lot of challenges. In order to overcome these shortcomings, a lane line detection model based on deep learning instance segmentation method was proposed. This model is based on the improved Mask R-CNN model. Firstly, the instance segmentation model was used to segment the lane line image, so as to improve the detection ability of lane line feature information. Then, the cluster model was used to extract the discrete feature information points of lane lines. Finally, an adaptive fitting method was proposed, and two fitting methods, linear and polynomial, were used to fit the feature points in different fields of view, and the optimal lane line parameter equation was generated. The experimental results show that the method improves the detection speed, has better detection accuracy in different scenes, and can achieve robust extraction of lane line information in various complex practical conditions.
[1] LECUN Y,BENGIO Y,HINTON G. Deep learning[J]. Nature, 2015,521(7553):436-444. [2] 张顺, 龚怡宏, 王进军. 深度卷积神经网络的发展及其在计算机视觉领域的应用[J]. 计算机学报,2019,42(3):453-482. (ZHANG S,GONG Y H,WANG J H. The development of deep convolution neural network and its applications on computer vision[J]. Chinese Journal of Computers,2019,42(3):453-482.) [3] 陈虹, 郭露露, 宫洵, 等. 智能时代的汽车控制[J]. 自动化学报, 2019,45(x):1-20.(CHEN H,GUO L L,GONG X,et al. Automotive control in intelligent era[J]. Acta Automatica Sinica,2019, 45(x):1-20.) [4] SIMONYAN K,ZISSERMAN A. Very deep convolutional networks for large-scale image recognition[EB/OL].[2019-04-10]. https://arxiv.org/pdf/1409.1556.pdf. [5] HE K,ZHANG X,REN S,et al. Deep residual learning for image recognition[C]//Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE,2016:770-778. [6] LONG J,SHELHAMER E,DARRELL T. Fully convolutional networks for semantic segmentation[C]//Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE,2015:3431-3440. [7] CHEN L C,PAPANDREOU G,KOKKINOS I,et al. Semantic image segmentation with deep convolutional nets and fully connected CRFs[EB/OL].[2019-05-12]. https://arxiv.org/pdf/1412.7062.pdf. [8] 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. [9] 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. [10] 宋小娜, 芮挺, 王新晴. 结合语义边界信息的道路环境语义分割方法[J]. 计算机应用,2019,39(9):2505-2510. (SONG X N,RUI T,WANG X Q. Semantic segmentation method of road environment combined semantic boundary information[J]. Journal of Computer Applications,2019,39(9):2505-2510.) [11] PASZKE A,CHAURASIA A,KIM S,et al. ENet:a deep neural network architecture for real-time semantic segmentation[EB/OL].[2019-06-07]. https://arxiv.org/pdf/1606.02147.pdf. [12] 田锦, 张弛, 王永森, 等. 基于Mask R-CNN的地面标识检测[C]//中国计算机用户协会网络应用分会2018年第二十二届网络新技术与应用年会论文集. 北京:[出版者不详], 2018:59-62. (TIAN J,ZHANG C, WANG Y S. Road sign model based on Mask R-CNN[C]//Proceedings of the 22nd Annual Conference on New Network Technologies and Applications 2018,Network Application Branch,China Computer Users Association,China Computer User Association. Beijing:[s. n.],2018:59-62.) [13] REN S,HE K,GIRSHICK R,et al. Faster R-CNN:towards realtime object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2017, 39(6):1137-1149. [14] REDMON J,FARHADI A. YOLOV3:an incremental improvement[EB/OL].[2019-04-08]. https://arxiv.org/pdf/1804.02767.pdf. [15] ALSENAN S,YKHLEF M. Statistical machine translation context modelling with recurrent neural network and LDA[C]//Proceedings of the 2016 International Conference on Advanced Intelligent Systems and Informatics,AISC 533. Cham:Springer,2016:75-84. [16] HERMANTO A,ADJI T B,SETIAWAN N A. Recurrent neural network language model for English-Indonesian machine translation:experimental study[C]//Proceedings of the 2015 International Conference on Science in Information Technology. Piscataway:IEEE,2015:132-136. [17] HAIDAR M A,KURIMO M. LDA-based context dependent recurrent neural network language model using document-based topic distribution of words[C]//Proceedings of the 2017 IEEE International Conference on Acoustics,Speech and Signal Processing. Piscataway:IEEE,2017:5730-5734. [18] KIM J,LEE M. Robust lane detection based on convolutional neural network and random sample consensus[C]//Proceedings of the 21st International Conference on Neural Information Processing, LNCS 8834. Cham:Springer,2014:454-461. [19] HE B,AI R,YAN Y,et al. Accurate and robust lane detection based on dual-view convolutional neutral network[C]//Proceedings of the 2016 IEEE Intelligent Vehicles Symposium. Piscataway:IEEE,2016:1041-1046. [20] BAILO O,LEE S,RAMEAU F,et al. Robust road marking detection and recognition using density-based grouping and machine learning techniques[C]//Proceedings of the 2017 IEEE Winter Conference on Applications of Computer Vision. Piscataway:IEEE,2017:760-768. [21] GURGHIAN A,KODURI T,BAILUR S V,et al. DeepLanes:end-to-end lane position estimation using deep neural networks[C]//Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops. Piscataway:IEEE, 2016:38-45. [22] CHAN T H,JIA K,GAO S,et al. PCANet:a simple deep learning baseline for image classification?[J]. IEEE Transactions on Image Processing,2015,24(12):5017-5032. [23] LIU H,LI X. Sharp curve lane detection for autonomous driving[J]. Computing in Science and Engineering,2019,21(2):80-95. [24] BOUMEDIENE M,OUAMRI A,DAHNOUN N. Lane boundary detection and tracking using NNF and HMM approaches[C]//Proceedings of the 2007 IEEE Intelligent Vehicles Symposium. Piscataway:IEEE,2007:1107-1111. [25] CIMURS R,HWANG J,SUH I H. Bezier curve-based smoothing for path planner with curvature constraint[C]//Proceedings of the 1st IEEE International Conference on Robotic Computing. Piscataway:IEEE,2017:241-248. [26] HE K,GKIOXARI G,DOLLÁR P,et al. Mask R-CNN[C]//Proceedings of the 2017 IEEE International Conference on Computer Vision. Piscataway:IEEE,2017:2980-2988.
2020, Vol. 40 
