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基于主动红外滤光环视成像的车道线检测算法

Lane Detection Based on Active Infrared Filter and Around-View Imaging

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摘要

针对传统的车道线检测系统采用单路前视摄像头在夜晚场景下易受强光照干扰和在复杂场景下易出现误检漏检的问题, 提出一种基于主动红外滤光环视成像的车道线检测算法。在成像阶段, 利用4路基于主动红外滤光成像的车载摄像头采集车辆四周的场景信息, 再基于透视变换和图像融合得到具有360°俯视效果的环视图像。在车道线的检测阶段, 提出一种基于凝聚型层次聚类的车道线检测算法:1) 基于车道线的形状特征设计一种具有较强针对性的匹配模板用来提取车道线边缘点; 2) 以凝聚型层次聚类对边缘点聚类, 并以随机抽样一致性算法对车道线进行直线拟合; 3) 结合先验信息和卡尔曼滤波器进一步提高检测准确性。结果表明, 本文算法能够较好地消除车道线检测过程中的强光照影响, 并在一定程度上有效地降低了误检漏检率。

Abstract

Aiming at the problem that the conventional lane detection system uses a single-channel forward-looking camera under night scenes, which is susceptible to strong light interference and is prone to false detection and misdetection in complex scenes, we propose a lane detection method based on active infrared filter and around-view imaging. In the imaging stage, four-way vehicle-borne cameras based on active infrared filter are used to collect scene information around the vehicle, and then a look-around image with 360° overlooking effect is obtained based on perspective transformation and image fusion. In the detection phase of lane, a lane detection algorithm is proposed based on agglomerative hierarchical clustering. Firstly, based on the shape features of lane lines, a more pertinent template matching is designed to extract the edge points of the lane line. Then the edge points are clustered by agglomerative hierarchical clustering, and the lane is fitted by the random sample consensus algorithm. Finally, a priori information and Kalman filter are combined to further improve detection accuracy. The results show that the proposed algorithm can effectively eliminate the strong light effects during the detection of lanes and effectively reduce the false detection and missed detection rate to a certain extent.

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中图分类号:TP391.4

DOI:10.3788/LOP55.121014

所属栏目:图像处理

基金项目:国家自然科学基金(61671202,61573128)、国家重点研发计划(2016YFC0401606)

收稿日期:2018-06-26

修改稿日期:2018-07-03

网络出版日期:2018-07-12

作者单位    点击查看

成春阳:河海大学物联网工程学院, 江苏 常州 213022
黄渊博:河海大学物联网工程学院, 江苏 常州 213022
卢鑫:河海大学物联网工程学院, 江苏 常州 213022
徐灵丽:河海大学物联网工程学院, 江苏 常州 213022
李敏:河海大学物联网工程学院, 江苏 常州 213022
范新南:河海大学物联网工程学院, 江苏 常州 213022“世界水谷”与水世界生态文明协同创新中心, 江苏 南京 211100
张学武:河海大学物联网工程学院, 江苏 常州 213022“世界水谷”与水世界生态文明协同创新中心, 江苏 南京 211100

联系人作者:张学武(lab_112@126.com)

【1】Hillel A B, Lerner R, Dan L, et al. Recent progress in road and lane detection: a survey[J]. Machine Vision & Applications, 2014, 25(3): 727-745.

【2】McCall J C, Wipf D P, Trivedi M M, et al. Lane change intent analysis using robust operators and sparse bayesian learning[J]. IEEE Transactions on Intelligent Transportation Systems, 2007, 8(3): 431-440.

【3】Xu L, Oja E, Kultanen P. A new curve detection method: randomized Hough transform (RHT)[J]. Pattern Recognition Letters, 1990, 11(5): 331-338.

【4】Sun T Y, Tsai S J, Chan V. HSI color model based lane-marking detection[C]. IEEE Intelligent Transportation Systems Conference, 2006: 1168-1172.

【5】Cheng H Y, Jeng B S, Tseng P T, et al. Lane detection with moving vehicles in the traffic scenes[J]. IEEE Transactions on Intelligent Transportation Systems, 2006, 7(4): 571-582.

【6】Benmansour N, Labayrade R, Aubert D, et al. Stereovision-based 3D lane detection system: a model driven approach[C]. International IEEE Conference on Intelligent Transportation Systems, 2008: 182-188.

【7】Nedevschi S, Schmidt R, Graf T, et al. 3D lane detection system based on stereovision[C]. International IEEE Conference on Intelligent Transportation Systems, 2004: 161-166.

【8】Pomerleau D A. ALVINN: an autonomous land vehicle in a neural network[M]. Advances in Neural Information Processing Systems 1. San Francisco: Morgan Kaufmann Publishers Inc., 1989: 305-313.

【9】Kim Z. Robustlane detection and tracking in challenging scenarios[J]. IEEE Transactions on Intelligent Transportation Systems, 2008, 9(1): 16-26.

【10】Satzoda R K, Trivedi M M. Vision-based lane analysis: exploration of issues and approaches for embedded realization[C]. IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2013: 604-609.

【11】Lee C, Moon J H. Robustlane detection and tracking for real-time applications[J]. IEEE Transactions on Intelligent Transportation Systems, 2018: 1-6.

【12】Cheng C Y, Li M, Zhang X W, et al. A lane detection algorithm under complex scenes[C]. International Conference on Computer, Mechatronics and Electronic Engineering, 2017: 386-390.

【13】Cui G T, Wang J Z, Li J. Robust multilane detection and tracking in urban scenarios based on LIDAR and mono-vision[J]. IET Image Processing, 2014, 8(5): 269-279.

【14】Fox A, Kumar B V K V, Chen J Z, et al. Multi-lane pothole detection from crowdsourced undersampled vehicle sensor data[J]. IEEE Transactions on Mobile Computing, 2017, 16(12): 3417-3430.

【15】Li Q Q, Chen L, Li M, et al. A sensor-fusion drivable-region and lane-detection system for autonomous vehicle navigation in challenging road scenarios[J]. IEEE Transactions on Vehicular Technology, 2014, 63(2): 540-555.

【16】Felguera-Martin D, Gonzalez-Partida J T, Almorox-Gonzalez P, et al. Vehicular traffic surveillance and road lane detection using radar interferometry[J]. IEEE Transactions on Vehicular Technology, 2012, 61(3): 959-970.

【17】Zhang S Y, Xie F, Wang J H. Design of medium and long distance automotive lens for target recognition of vehicles[J]. Laser & Optoelectronics Progress, 2017, 54(10): 102201.
张思远, 谢飞, 王建华. 用于车辆目标识别的中远距离车载镜头设计[J]. 激光与光电子学进展, 2017, 54(10): 102201.

【18】Zhang W Z. Research on issue in vehicle active safety technology based on machine visual perception[D]. Jinan: Shandong University, 2015: 15-28.
张万枝. 机器视觉感知下的车辆主动安全技术若干问题研究[D]. 济南: 山东大学, 2015: 15-28.

【19】Zhuo N, Sun H Y, Zhang H J. A new approach for improvement of CCD imaging resolution[J]. Acta Optica Sinica, 2005, 25(6): 777-780.
卓宁, 孙华燕, 张海江. 一种新的提高CCD成像分辨率的方法[J]. 光学学报, 2005, 25(6): 777-780.

【20】He Y J, Li M, Zhang J L, et al. Infrared small target detection method based on correlation filter[J]. Acta Optica Sinica, 2016, 36(5): 0512001.
何玉杰, 李敏, 张金利, 等. 基于相关滤波器的红外弱小目标检测算法[J]. 光学学报, 2016, 36(5): 0512001.

【21】Zhang X W, Ding Y Q, Yan P. Vision inspection of metal surface defects based on infrared imaging[J]. Acta Optica Sinica, 2011, 31(3): 0312004.
张学武, 丁燕琼, 闫萍. 一种基于红外成像的强反射金属表面缺陷视觉检测方法[J]. 光学学报, 2011, 31(3): 0312004.

【22】Zhao T, Kang H L, Zhang Z P. Fast image mosaic algorithm based on area blocking and BRISK[J]. Laser & Optoelectronics Progress, 2018, 55(3): 031005.
赵婷, 康海林, 张正平. 结合区域分块的快速BRISK图像拼接算法[J]. 激光与光电子学进展, 2018, 55(3): 031005.

【23】Gan W Y, Li D Y, Wang J M. An hierarchical clustering method based on data fields[J]. Acta Electronica Sinica, 2006, 34(2): 258-262.
淦文燕, 李德毅, 王建民. 一种基于数据场的层次聚类方法[J]. 电子学报, 2006, 34(2): 258-262.

【24】Zeng T Y, Du F. Image super-resolution reconstruction based on hierarchical clustering[J]. Acta Optica Sinica, 2018, 38(4): 0410004.
曾台英, 杜菲. 基于层次聚类的图像超分辨率重建[J]. 光学学报, 2018, 38(4): 0410004.

【25】Gu X L, Yang M, Wang B, et al. Lane detection and recognition based on around-view system[J]. Journal of Huazhong University of Science and Technology (Nature Science Edition), 2013(z1): 233-236.
顾霄琳, 杨明, 王冰, 等. 基于环视的道路标线检测与识别[J]. 华中科技大学学报(自然科学版), 2013(z1): 233-236.

【26】Borkar A, Hayes M, Smith M T. A novel lane detection system with efficient ground truth generation[J]. IEEE Transactions on Intelligent Transportation Systems, 2012, 13(1): 365-374.

引用该论文

Cheng Chunyang,Huang Yuanbo,Lu Xin,Xu Lingli,Li Min,Fan Xinnan,Zhang Xuewu. Lane Detection Based on Active Infrared Filter and Around-View Imaging[J]. Laser & Optoelectronics Progress, 2018, 55(12): 121014

成春阳,黄渊博,卢鑫,徐灵丽,李敏,范新南,张学武. 基于主动红外滤光环视成像的车道线检测算法[J]. 激光与光电子学进展, 2018, 55(12): 121014

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