Computer Science

Computer Science ›› 2020, Vol. 47 ›› Issue (4): 142-149.doi: 10.11896/jsjkx.190500021

• Computer Graphics & Multimedia • Previous Articles     Next Articles

Lane Detection Algorithm Based on Improved Enet Network

LIU Bin, LIU Hong-zhe   

  1. Key Laboratory of Information Service Engineering,Beijing Union University,Beijing 100101,China
  • Received:2019-05-06 Online:2020-04-15 Published:2020-04-15
  • Contact: LIU Hong-zhe,born in 1971,Ph.D,professor,Ph.D supervisor,is a member of China Computer Federation.Her main research interests include visual computing and deep learning.
  • About author:LIU Bin,born in 1991,postgraduate.His main research interests include digi-tal image processing and deep learning.
  • Supported by:
    This work was supported by the National Natural Science Foundation of China (61871039,61802019,61906017),Supporting Plan for Cultivating High Level Teachers in Colleges and Universities in Beijing (IDHT20170511),Beijing Natural Science Foundation(4184088),Premium Funding Project for Academic Human Resources Development in Beijing Union University (BPHR2019AZ01),Beijing Municipal Commission of Education Project (KM201911417001,KM201711417005),National Key Technology R&D Program (2015BAH55F03) and Big Data Collaborative Innovation Center for Intelligent Driving (CYXC1902)

PDF (PC)

2170

Abstract: 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 testedin 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 requirementsin 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 achievedrealtime detection results.

Key words: Adaptive fitting, Clustering, Image semantics segmentation, Lane detection

CLC Number: 

  • TP391.4
[1]CHANG C Y,LIN C H.An Efficient Method for Lane-mark Extraction in Complex Conditions[C]// 2012 9th International Conference on Ubiquitous Intelligence and Computing and 9th International Conference on Autonomic and Trusted Computing.IEEE,2012:330-336.
[2]LEE C,DING D.An Adaptive Road ROI Determination Algorithm for Lane Detection[J].Journal of the Institute of Electronics and Information Engineers,2014,51(1):116-125.
[3]BENLIGIRAY B,TOPAL C,AKINLAR C.Video-Based Lane Detection Using a Fast Vanishing Point Estimation Method[C]//IEEE International Symposium on Multimedia.IEEE,2012:348-351.
[4]WANG J Y,DUAN J M.Lane Detection Algorithm Using Vanishing Point[C]//2013 International Conference on Machine Learning and Cybernetics.IEEE,2013,2:735-740.
[5]LI W,GONG X,WANG Y,et al.A Lane Marking Detection and Tracking Algorithm Based on Sub-Regions[C]// Proceedings 2014 International Conference on Informative and Cybernetics for Computational Social Systems (ICCSS).IEEE,2014:68-73.
[6]TAN H,ZHOU Y,ZHU Y,et al.A novel curve lane detection based on Improved River Flow and RANSA[C]//17th International IEEE Conference on Intelligent Transportation Systems (ITSC).IEEE,2014:133-138.
[7]LI H,FENG M,WANG X.Inverse Perspective Mapping Based Urban Road Markings Detection [C]//2012 IEEE 2nd International Conference on Cloud Computing and Intelligence Systems.IEEE,2012:1178-1182.
[8]MU C,MA X.Lane Detection Based on Object Segmentationand Piecewise Fitting[J].Telkomnika Indonesian Journal of Electrical Engineering,2014,12(5):3491-3500.
[9]KIM J,LEE M.Robust Lane Detection Based on Convolutional Neural Network and Random Sample Consensus[C]//International Conference on Neural Information Processing.Cham:Springer,2014:454-461.
[10]HUVAL B,WANG T,TANDON S,et al.An Empirical Evaluation of Deep Learning on Highway Driving[J].arXiv:1504.01716.
[11]LI J,MEI X,PROKHOROV D,et al.Deep Neural Network for Structural Prediction and Lane Detection in Traffic Scene[J].IEEE Transactions on Neural Networks and Learning Systems,2017,28(3):690-703.
[12]HE B,AI R,YAN Y,et al.Accurate and Robust Lane DetectionBased on Dual-View Convolutional Neutral Network [C]//2016 IEEE Intelligent Vehicles Symposium (IV).IEEE,2016:1041-1046.
[13]BAILO O,LEE S,RAMEAU F,et al.Robust Road Marking Detection and Recognition Using Density-based Grouping and Machine Learning Techniques[C]//2017 IEEE Winter Confe-rence on Applications of Computer Vision (WACV).IEEE,2017:760-768.
[14]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.
[15]LEE S,KIM J,YOON J S,et al.VPGNet:Vanishing PointGuided Network for Lane and Road Marking Detection and Recognition[J].arXiv:1710.06288,2017.
[16]PASZKE A,CHAURASIA A,KIM S,et al.ENet:A Deep Neural Network Architecture for Real-Time Semantic Segmentation[J].arXiv:1606.02147,2016.
[17]IOFFE S,SZEGEDY C.Batch Normalization:Accelerating Deep Network Training by Reducing Internal Covariate Shift[C]// International Conference on International Conference on Machine Learning.JMLR.org,2015:1-11.
[18]HE K,ZHANG X,REN S,et al.Delving Deep into Rectifiers:Surpassing Human-level Performance on Imagenet Classification[C]//Proceedings of the IEEE International Conference on Computer Vision.2015:1026-1034.
[19]TOMPSON J,GOROSHIN R,JAIN A,et al.Efficient ObjectLocalization Using Convolutional Networks[C]//IEEE Confe-rence on Computer Vision and Pattern Recognition.2015:648-656.
[20]PAN X,SHI J,LUO P,et al.Spatial As Deep:Spatial CNN for Traffic Scene Understanding[C]// IEEE Conference on Computer Vision and Pattern Recognition.IEEE,2017:1-8.
[1] LU Chen-yang, DENG Su, MA Wu-bin, WU Ya-hui, ZHOU Hao-hao. Federated Learning Based on Stratified Sampling Optimization for Heterogeneous Clients [J]. Computer Science, 2022, 49(9): 183-193.
[2] CHAI Hui-min, ZHANG Yong, FANG Min. Aerial Target Grouping Method Based on Feature Similarity Clustering [J]. Computer Science, 2022, 49(9): 70-75.
[3] YU Shu-hao, ZHOU Hui, YE Chun-yang, WANG Tai-zheng. SDFA:Study on Ship Trajectory Clustering Method Based on Multi-feature Fusion [J]. Computer Science, 2022, 49(6A): 256-260.
[4] MAO Sen-lin, XIA Zhen, GENG Xin-yu, CHEN Jian-hui, JIANG Hong-xia. FCM Algorithm Based on Density Sensitive Distance and Fuzzy Partition [J]. Computer Science, 2022, 49(6A): 285-290.
[5] CHEN Jing-nian. Acceleration of SVM for Multi-class Classification [J]. Computer Science, 2022, 49(6A): 297-300.
[6] CHEN Jia-zhou, ZHAO Yi-bo, XU Yang-hui, MA Ji, JIN Ling-feng, QIN Xu-jia. Small Object Detection in 3D Urban Scenes [J]. Computer Science, 2022, 49(6): 238-244.
[7] Ran WANG, Jiang-tian NIE, Yang ZHANG, Kun ZHU. Clustering-based Demand Response for Intelligent Energy Management in 6G-enabled Smart Grids [J]. Computer Science, 2022, 49(6): 44-54.
[8] XING Yun-bing, LONG Guang-yu, HU Chun-yu, HU Li-sha. Human Activity Recognition Method Based on Class Increment SVM [J]. Computer Science, 2022, 49(5): 78-83.
[9] ZHU Zhe-qing, GENG Hai-jun, QIAN Yu-hua. Line-Segment Clustering Algorithm for Chemical Structure [J]. Computer Science, 2022, 49(5): 113-119.
[10] ZHANG Yu-jiao, HUANG Rui, ZHANG Fu-quan, SUI Dong, ZHANG Hu. Study on Affinity Propagation Clustering Algorithm Based on Bacterial Flora Optimization [J]. Computer Science, 2022, 49(5): 165-169.
[11] ZUO Yuan-lin, GONG Yue-jiao, CHEN Wei-neng. Budget-aware Influence Maximization in Social Networks [J]. Computer Science, 2022, 49(4): 100-109.
[12] YANG Xu-hua, WANG Lei, YE Lei, ZHANG Duan, ZHOU Yan-bo, LONG Hai-xia. Complex Network Community Detection Algorithm Based on Node Similarity and Network Embedding [J]. Computer Science, 2022, 49(3): 121-128.
[13] HAN Jie, CHEN Jun-fen, LI Yan, ZHAN Ze-cong. Self-supervised Deep Clustering Algorithm Based on Self-attention [J]. Computer Science, 2022, 49(3): 134-143.
[14] PU Shi, ZHAO Wei-dong. Community Detection Algorithm for Dynamic Academic Network [J]. Computer Science, 2022, 49(1): 89-94.
[15] ZHANG Ya-di, SUN Yue, LIU Feng, ZHU Er-zhou. Study on Density Parameter and Center-Replacement Combined K-means and New Clustering Validity Index [J]. Computer Science, 2022, 49(1): 121-132.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
渝ICP备16013121号-4
Add:18# Honghu West Rd., Yubei ,Chongqing,China    Post Code: 401121
Tel: 023-63500828/023-67039612/023-67039623
E-mail: jsjkx12@163.com
Powered by Beijing Magtech Co., Ltd.