基于智能车辆视觉导航的道路检测技术的研究
|
摘要
近年来,视觉辅助导航已成为智能车辆导航领域的研究热点之一,其中道路检测是视觉辅助导航中的关键技术,道路图像的检测结果会受到噪声质量及成像质量的严重影响。本文以道路检测技术中将会遇到的几个关键问题作为研究重点,对道路图像进行增强、边缘检测等算法的处理,主要研究了基于图像处理的道路检测技术。
本文根据道路图像的噪声类型,针对不同的噪声采取相应地去噪方案,提出一种改进的直方图均衡算法,可以有效地解决图像的模糊问题;而对于雨雪天气造成的椒盐噪声,利用中值滤波能够有效地去除其噪声;对于高斯白噪声,则采用自适应滤波的滤波效果最好。
在图像边缘检测中,通过对经典边缘检测算法的具体分析与比较,提出一种基于多尺度几何分析的Beamlet变换的边缘检测算法。该算法符合最优图像特征的表示方法,能够充分利用图像的几何正则性,检测出的道路边缘更加连续清晰,使道路的边缘特征得到较好的保护。在阈值分割环节,通过对几种传统阈值分割方法的比较,根据道路图像特点,确定使用最大类间方差法进行阈值分割。
在进行道路边界线提取时,采用一种基于直线点密度的霍夫变换方法,将图像空间和霍夫空间有机地统一起来,该方法能够避免由于过度积累而导致伪直线过多的缺点,同时具有较好的鲁棒性和抗噪性,从而提高道路检测的准确性。
文中通过对采集的道路图像添加椒盐噪声、高斯白噪声以及模糊化的处理,模拟实时道路检测中可能会遇到的影响图像质量的问题,仿真实验结果论证本文所提出的算法能够准确地检测出道路图像中的车道线,提高道路检测的识别率,证明文中算法的有效性和可靠性,为后续的研究工作提供有用的道路信息。
Recently, road detection technique is a very important part of vision navigation, which is the key technique of intelligent vehicle guidance. The detection result is seriously affected by the quality of noise and image. Several key problems in the road detection technique are chosen as the emphasis of this paper, and then modify the road image quality by a series of changes in image enhancement, edge detection and so on. This paper analyses and researches the methods of road detection technique based image process.
This paper analysis the type of noise, put forward the right denoise method for different types of noise, and then proposed an improvement in histogram equalization, which can effectively solve the blurring problems in the image. For certain degree influence regarding the path recognition under the sleet condition to use spiced salt noise to carry on the simulation to it. The discovery uses automatic filter the effect to white Gaussian noise is better.
Through the specific analysis and comparison of various edge detection algorithm, the image edge detection algorithm based on multiscale geometry analyse is used to extract the edge outline. The new edge detection algorithm can make use of the geometrical regularity better and conform to best practices in image picture attribution. By the simulate experiments, the method processes lubricity and series in the edge, the texture of road edge is protected better. In image segmentation, this paper study server traditional threshold segmentation algorithm, and use maximum classes square error in this paper.
In road boundary line extraction, use Hough transform based on Line Point Density, which can solve the accumulating problem of classical Hough transform. This method also have nice anti noise performance and robustness, and can effectively extract lines form road image.
Add spiced salt noise, white Gaussian noise and fuzzy treatment methods in the real-time simulation of the road likely to be encountered in the quality of the image, then pretreatment for road detection. The proof shows that the algorithm in this paper can effectively improve the recognition rate in road detection, accurately extract lines form road image, and proved the efficiency and the reliability of this approach. It also provided useful road information for follow up study.
本文根据道路图像的噪声类型,针对不同的噪声采取相应地去噪方案,提出一种改进的直方图均衡算法,可以有效地解决图像的模糊问题;而对于雨雪天气造成的椒盐噪声,利用中值滤波能够有效地去除其噪声;对于高斯白噪声,则采用自适应滤波的滤波效果最好。
在图像边缘检测中,通过对经典边缘检测算法的具体分析与比较,提出一种基于多尺度几何分析的Beamlet变换的边缘检测算法。该算法符合最优图像特征的表示方法,能够充分利用图像的几何正则性,检测出的道路边缘更加连续清晰,使道路的边缘特征得到较好的保护。在阈值分割环节,通过对几种传统阈值分割方法的比较,根据道路图像特点,确定使用最大类间方差法进行阈值分割。
在进行道路边界线提取时,采用一种基于直线点密度的霍夫变换方法,将图像空间和霍夫空间有机地统一起来,该方法能够避免由于过度积累而导致伪直线过多的缺点,同时具有较好的鲁棒性和抗噪性,从而提高道路检测的准确性。
文中通过对采集的道路图像添加椒盐噪声、高斯白噪声以及模糊化的处理,模拟实时道路检测中可能会遇到的影响图像质量的问题,仿真实验结果论证本文所提出的算法能够准确地检测出道路图像中的车道线,提高道路检测的识别率,证明文中算法的有效性和可靠性,为后续的研究工作提供有用的道路信息。
Recently, road detection technique is a very important part of vision navigation, which is the key technique of intelligent vehicle guidance. The detection result is seriously affected by the quality of noise and image. Several key problems in the road detection technique are chosen as the emphasis of this paper, and then modify the road image quality by a series of changes in image enhancement, edge detection and so on. This paper analyses and researches the methods of road detection technique based image process.
This paper analysis the type of noise, put forward the right denoise method for different types of noise, and then proposed an improvement in histogram equalization, which can effectively solve the blurring problems in the image. For certain degree influence regarding the path recognition under the sleet condition to use spiced salt noise to carry on the simulation to it. The discovery uses automatic filter the effect to white Gaussian noise is better.
Through the specific analysis and comparison of various edge detection algorithm, the image edge detection algorithm based on multiscale geometry analyse is used to extract the edge outline. The new edge detection algorithm can make use of the geometrical regularity better and conform to best practices in image picture attribution. By the simulate experiments, the method processes lubricity and series in the edge, the texture of road edge is protected better. In image segmentation, this paper study server traditional threshold segmentation algorithm, and use maximum classes square error in this paper.
In road boundary line extraction, use Hough transform based on Line Point Density, which can solve the accumulating problem of classical Hough transform. This method also have nice anti noise performance and robustness, and can effectively extract lines form road image.
Add spiced salt noise, white Gaussian noise and fuzzy treatment methods in the real-time simulation of the road likely to be encountered in the quality of the image, then pretreatment for road detection. The proof shows that the algorithm in this paper can effectively improve the recognition rate in road detection, accurately extract lines form road image, and proved the efficiency and the reliability of this approach. It also provided useful road information for follow up study.
引文
[1]林鹰. ITS系统及其实现的关键技术[J].计算机控制、检测与管理. 2004,(4):54～59
[2] Bishop,R. A Survery of Intelligent Vehicle Applications Worldwide. IEEE. Intelligent Vehicle Symposium. 2003,(3):25～30
[3] Dirk J. Verburg, Albert C. M. Vander Knap, Jeroen. Developing and Testing Intelligent Vehicles. IEEE Intelligent Vehicle, June 2002,(2):537～544
[4]谢飞.未来智能汽车及智能汽车交通系统[J].汽车技术.
[5]栗菊.智能交通系统(ITS)在国外[J].中国机电工业. 2000,9:21～22
[6]杨殿阁,李克强,郑四发等.智能交通系统中的汽车技术[J].汽车工程. 2003,25(3):223～226
[7] Dickmanns E D. Exportation-Based Multi Focal Vision for Vehicle Guidance. Proc of Singal Processing Conference. 1995,1023～1026
[8] Jochen T M, Baluja S. A Massively Parallel Road Follower. Computer Architecture for Machine Perception. 1993,(11):2～12
[9]刘化胜.智能车辆视觉导航中道路检测算法的研究[D].硕士学位论文.南京:南京航空航天大学,2004
[10] Massimo Bertozzi, Alberto Broggi, Massimo Cellario. Artificial Vision in Road Vehicles. Proceedings of the IEEE. 2002,90(7):1258～1271
[11] Ran, B., Liu, H. Development of A Vision-based Vehicle Detection and Recognition System for Intelligent Vehicle. Transportation Research Record 79th Annual Meeting. Washington DC. 2000,130～138
[12]王荣本,李兵,施树明等.世界智能车辆研究概述[J].公路交通科技. 2001, 18(5):93～97
[13] Michelle Bayouth, Illah R. Nourbakhsh, Chuck E. Thorpe. A Hybrid Human-Computer Autonomous Vehicle Architecture. 1999
[14] P. Bellutta, R.Manduchi, L Matthies, etal. Terrain Perception for DEMOⅢ. Proceedings of the IEEE Intelligent Vehicles Symposium. 2000:326～331
[15] James S. Albus. 4-D/RCS: A Reference Model Architecture
[16] Jason W. Grzywna, Dr. Eric M. Schwartz, Dr. A. Antonio Arroyo. KELVIN: Kart for Long distance Vision Navigation.Conference on Recent Advances in Robotics.2001
[17] Jason W. Grzywna. KELVIN project Vehicle Navigation using Stereo Computer Vision. 2001
[18] Rudolf Gregor, M. Lützeler, M. Pellkofer, etal. EMS-Vision: A Perceptual System for Autonomous Vehicles. IEEE Transactions On Intelligent Transportation Systems. 2002,3(1):48～59
[19] Alberto Broggi, Massimo Bertozzi, Alessandra Fascioli , etal. Automatic Vehicle Guiance:the Experience of the ARGO Autonomous Vehicle. 1999
[20] Jin Woo Lee, Jung Ho Kin, Young Jin Lee. A Study on Recognition of Lane and Movement of Vehicles for port AGV Vision System, Proc. IEEE. 2002:463～466
[21] Jin-Woo Lee, Sung-UK Choi, Young-Jin Lee. A Study on Recognition of Road and Movement of Vehicles using Vision System. Proc.SICE. 2001:38～41
[22] Seung Gweon Jeong, Chang Sup Kim, Kang Sup Yoon. Real-Time Lane Detection for Autonomous Navigation. IEEE Intelligent Transportation System Conference Proceedings. 2001:508～513
[23] Gang Yi Jiang, Tae Young Choi, Suk Kyo Hong. Lane and Obstacle Detection Based on Fast Inverse Perspective Mapping Algorithm, Proc. IEEE. 2000:2969～2974
[24] Jeong. D.Y., Park. S.J., Han. S. H, A study on neural networks for vision-based road following of autonomous vehicle. Industrial Electronics. IEEE International Symposium. 2001,(3):1609～1614
[25] Kim. K.I., Oh. S.Y., Kim. S.W. An autonomous land vehicle PRV III, Intelligent Vehicles Symposium, Proceedings of the IEEE. 1996:159～164
[26] Park. S.T., Yang. S.Y., Jung. J.H. Real-time lane recognition by simulated annealing algorithm. Science and Technology, KORUS ,The 4th Korea-Russia International Symposium. 2000,(3):95～98
[27] Seung Gweon Jeong, Chang Sup Kim, Dong Youp Lee. Real-Time Lane Detection for Autonomous Vehicle. Proc.IEEE. 2001:466～1471
[28] Masatoshi Okutomi, Katsuyuki Nakano, Junichi Maruyama. Robust Estimation of Planar Regions for Vision Navigation using Sequential Stereo Images. Proceedings of the IEEE International Conference on Robotics &Automation. 2002:3321～3327
[29] Inhyuk Moon , Jun Miura, Yoshiaki Shirai. On-line Viewpoint and Motion Planning for Efficient Visual Navigation under Uncertainty. Robotics and Autonomous Systems. 1999,(28):237～248
[30] W.S.Wijesoma, K.R.S. Kodagoda, A.P.Balasuriya. Road Edge and Lane Boundary Detection using Laser and Vision. Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems. 2001:1140～1145
[31]孙振平,安向京,贺汉根. CITAVT-IV——视觉导航的自主车[J].机器人. 2002,24(2):115～121
[32]徐文渊,朱淼良.分布式系统调试器IVD的系统实现[J].浙江大学学报(工学版). 2002,36(2):166～171
[33]李介谷等.图像处理技术[M].上海:上海交通大学出版社. 1988
[34] Pratt W K. Digital Image Processing. New York: John Wiley & Sons, Inc. 1978
[35] Rosenfeld A, Kak A C. Digital Picture Processing. 2nd Edition. New York: Academic Press. 1982,209～265
[36] Castleman K R. Digital Image Processing. New Jersey: Prentice-Hall, Englewood Cliffs. 1979
[37] Gonzalez R C, Wintz P. Digital Picture Processing. Massachusetts: Addison-Wesley. Reading. 1977
[38]傅德胜,寿亦禾.图形图像处理学[M].南京:东南大学出版社. 2001:160～178
[39]唐志文,林亚平,彭胜标等.改变位权值的直方图均衡算法研究和应用[J].湖南大学学报. 2001,2
[40]焦李成,谭山.图像的多尺度几何分析:回顾和展望[J].电子学报. 2003,31(12A): 2～3
[41] Donoho D, Huo X. Beamlets and Multiscale Image Analysis. Multiscale and Multiresolution Methods[A]. In: Computational Science and Engineering[C],New York NY USA, 2002,(20):149～196
[42] David L Donoho. Wedgelets: Nearly minimax estimation of edges[J]. The Annals of Statistics. 1999,27(3):859～897
[43] Roberts L G.“Machine Perception of Three-Dimensional Solids.”in: Tippett J T,ed.,Optical and Electro-Optical Information Processing, Cambridge, MA: MIT Press. 1965:159～197
[44]孙即祥等.图像分析[M].北京:科学出版社. 2005:16～90
[45]赖志国等. Matlab图像处理与应用[M].北京:国防工业出版. 2007,4:168～199
[46] Prewitt, J,“Object Enhancement and Extraction.”In: Lipkin B and Rosenfeld A, eds. Picture Processing and Psychopictorics, New York: Academic Press. 1970
[47] Canny J. A Computational Approach to Edge Detection[J]. IEEET Trans on PAMI.1986,8(6):679～698
[48]陈书海,傅录祥等.实用数字图像处理[M].北京:科学出版社. 2005:1～150
[49] Donoho D, Huo X. Beamlets and Multiscale Image Analysis. Multiscale and Multiresolution Methods[A]. In: Computational Science and Engineering[C],New York NY USA. 2002,(20):149～196
[50] J Elder. Are edges incomplete. International Journal of Computer Vision[J]. 1999,34(2):97～122
[51] Bresenham J E. Algorithm for Computer Control of A Digital Plotter[J]. IBM Systems Journal. 1965,4(1):25～30
[52] LA.D.Reading,C.L.Wan,K.W.Dickinson. Pedestrian Detection at Road Crossings by Computer Vision. IEEE Road Transport Information and Control. 1998,2(3):139～143
[53]章毓晋.图像分割[M].北京:科学出版社. 2001:1～9
[54]周金萍. MATLAB 6.5图形图像处理与应用[M].科学出版社. 2003:87～265
[55] Alberto Broggi, Alessandra Fascioli. Artificial Vision in Extreme Environments for Snowcat Tracks Detection. IEEE Transaction on Intelligent Transportation System,September 2002,3(3):162～172
[56]吴宇岚,费旭东,荆仁杰.边界的快速跟踪算法和硬件实现[J].通信学报. 1997.5
[57]章毓晋.图像工程上册:图像处理和分析[M].北京:清华大学出版社. 1999:107～115
[58]孙亚兵.城市CCD图像关键目标区域提取相关技术研究[D].硕士学位论文.青岛:中国海洋大学. 2008
[59]唐亮,谢维信,黄建军,黄敬雄.自适应模糊Hough变换[J].电子学报. 2004,32(6):946～949
[60]陈燕新,戚飞虎.利用梯度方向信息的随机Hough变换[J].红外与毫米波学报. 1998,17(5):375～380
[2] Bishop,R. A Survery of Intelligent Vehicle Applications Worldwide. IEEE. Intelligent Vehicle Symposium. 2003,(3):25～30
[3] Dirk J. Verburg, Albert C. M. Vander Knap, Jeroen. Developing and Testing Intelligent Vehicles. IEEE Intelligent Vehicle, June 2002,(2):537～544
[4]谢飞.未来智能汽车及智能汽车交通系统[J].汽车技术.
[5]栗菊.智能交通系统(ITS)在国外[J].中国机电工业. 2000,9:21～22
[6]杨殿阁,李克强,郑四发等.智能交通系统中的汽车技术[J].汽车工程. 2003,25(3):223～226
[7] Dickmanns E D. Exportation-Based Multi Focal Vision for Vehicle Guidance. Proc of Singal Processing Conference. 1995,1023～1026
[8] Jochen T M, Baluja S. A Massively Parallel Road Follower. Computer Architecture for Machine Perception. 1993,(11):2～12
[9]刘化胜.智能车辆视觉导航中道路检测算法的研究[D].硕士学位论文.南京:南京航空航天大学,2004
[10] Massimo Bertozzi, Alberto Broggi, Massimo Cellario. Artificial Vision in Road Vehicles. Proceedings of the IEEE. 2002,90(7):1258～1271
[11] Ran, B., Liu, H. Development of A Vision-based Vehicle Detection and Recognition System for Intelligent Vehicle. Transportation Research Record 79th Annual Meeting. Washington DC. 2000,130～138
[12]王荣本,李兵,施树明等.世界智能车辆研究概述[J].公路交通科技. 2001, 18(5):93～97
[13] Michelle Bayouth, Illah R. Nourbakhsh, Chuck E. Thorpe. A Hybrid Human-Computer Autonomous Vehicle Architecture. 1999
[14] P. Bellutta, R.Manduchi, L Matthies, etal. Terrain Perception for DEMOⅢ. Proceedings of the IEEE Intelligent Vehicles Symposium. 2000:326～331
[15] James S. Albus. 4-D/RCS: A Reference Model Architecture
[16] Jason W. Grzywna, Dr. Eric M. Schwartz, Dr. A. Antonio Arroyo. KELVIN: Kart for Long distance Vision Navigation.Conference on Recent Advances in Robotics.2001
[17] Jason W. Grzywna. KELVIN project Vehicle Navigation using Stereo Computer Vision. 2001
[18] Rudolf Gregor, M. Lützeler, M. Pellkofer, etal. EMS-Vision: A Perceptual System for Autonomous Vehicles. IEEE Transactions On Intelligent Transportation Systems. 2002,3(1):48～59
[19] Alberto Broggi, Massimo Bertozzi, Alessandra Fascioli , etal. Automatic Vehicle Guiance:the Experience of the ARGO Autonomous Vehicle. 1999
[20] Jin Woo Lee, Jung Ho Kin, Young Jin Lee. A Study on Recognition of Lane and Movement of Vehicles for port AGV Vision System, Proc. IEEE. 2002:463～466
[21] Jin-Woo Lee, Sung-UK Choi, Young-Jin Lee. A Study on Recognition of Road and Movement of Vehicles using Vision System. Proc.SICE. 2001:38～41
[22] Seung Gweon Jeong, Chang Sup Kim, Kang Sup Yoon. Real-Time Lane Detection for Autonomous Navigation. IEEE Intelligent Transportation System Conference Proceedings. 2001:508～513
[23] Gang Yi Jiang, Tae Young Choi, Suk Kyo Hong. Lane and Obstacle Detection Based on Fast Inverse Perspective Mapping Algorithm, Proc. IEEE. 2000:2969～2974
[24] Jeong. D.Y., Park. S.J., Han. S. H, A study on neural networks for vision-based road following of autonomous vehicle. Industrial Electronics. IEEE International Symposium. 2001,(3):1609～1614
[25] Kim. K.I., Oh. S.Y., Kim. S.W. An autonomous land vehicle PRV III, Intelligent Vehicles Symposium, Proceedings of the IEEE. 1996:159～164
[26] Park. S.T., Yang. S.Y., Jung. J.H. Real-time lane recognition by simulated annealing algorithm. Science and Technology, KORUS ,The 4th Korea-Russia International Symposium. 2000,(3):95～98
[27] Seung Gweon Jeong, Chang Sup Kim, Dong Youp Lee. Real-Time Lane Detection for Autonomous Vehicle. Proc.IEEE. 2001:466～1471
[28] Masatoshi Okutomi, Katsuyuki Nakano, Junichi Maruyama. Robust Estimation of Planar Regions for Vision Navigation using Sequential Stereo Images. Proceedings of the IEEE International Conference on Robotics &Automation. 2002:3321～3327
[29] Inhyuk Moon , Jun Miura, Yoshiaki Shirai. On-line Viewpoint and Motion Planning for Efficient Visual Navigation under Uncertainty. Robotics and Autonomous Systems. 1999,(28):237～248
[30] W.S.Wijesoma, K.R.S. Kodagoda, A.P.Balasuriya. Road Edge and Lane Boundary Detection using Laser and Vision. Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems. 2001:1140～1145
[31]孙振平,安向京,贺汉根. CITAVT-IV——视觉导航的自主车[J].机器人. 2002,24(2):115～121
[32]徐文渊,朱淼良.分布式系统调试器IVD的系统实现[J].浙江大学学报(工学版). 2002,36(2):166～171
[33]李介谷等.图像处理技术[M].上海:上海交通大学出版社. 1988
[34] Pratt W K. Digital Image Processing. New York: John Wiley & Sons, Inc. 1978
[35] Rosenfeld A, Kak A C. Digital Picture Processing. 2nd Edition. New York: Academic Press. 1982,209～265
[36] Castleman K R. Digital Image Processing. New Jersey: Prentice-Hall, Englewood Cliffs. 1979
[37] Gonzalez R C, Wintz P. Digital Picture Processing. Massachusetts: Addison-Wesley. Reading. 1977
[38]傅德胜,寿亦禾.图形图像处理学[M].南京:东南大学出版社. 2001:160～178
[39]唐志文,林亚平,彭胜标等.改变位权值的直方图均衡算法研究和应用[J].湖南大学学报. 2001,2
[40]焦李成,谭山.图像的多尺度几何分析:回顾和展望[J].电子学报. 2003,31(12A): 2～3
[41] Donoho D, Huo X. Beamlets and Multiscale Image Analysis. Multiscale and Multiresolution Methods[A]. In: Computational Science and Engineering[C],New York NY USA, 2002,(20):149～196
[42] David L Donoho. Wedgelets: Nearly minimax estimation of edges[J]. The Annals of Statistics. 1999,27(3):859～897
[43] Roberts L G.“Machine Perception of Three-Dimensional Solids.”in: Tippett J T,ed.,Optical and Electro-Optical Information Processing, Cambridge, MA: MIT Press. 1965:159～197
[44]孙即祥等.图像分析[M].北京:科学出版社. 2005:16～90
[45]赖志国等. Matlab图像处理与应用[M].北京:国防工业出版. 2007,4:168～199
[46] Prewitt, J,“Object Enhancement and Extraction.”In: Lipkin B and Rosenfeld A, eds. Picture Processing and Psychopictorics, New York: Academic Press. 1970
[47] Canny J. A Computational Approach to Edge Detection[J]. IEEET Trans on PAMI.1986,8(6):679～698
[48]陈书海,傅录祥等.实用数字图像处理[M].北京:科学出版社. 2005:1～150
[49] Donoho D, Huo X. Beamlets and Multiscale Image Analysis. Multiscale and Multiresolution Methods[A]. In: Computational Science and Engineering[C],New York NY USA. 2002,(20):149～196
[50] J Elder. Are edges incomplete. International Journal of Computer Vision[J]. 1999,34(2):97～122
[51] Bresenham J E. Algorithm for Computer Control of A Digital Plotter[J]. IBM Systems Journal. 1965,4(1):25～30
[52] LA.D.Reading,C.L.Wan,K.W.Dickinson. Pedestrian Detection at Road Crossings by Computer Vision. IEEE Road Transport Information and Control. 1998,2(3):139～143
[53]章毓晋.图像分割[M].北京:科学出版社. 2001:1～9
[54]周金萍. MATLAB 6.5图形图像处理与应用[M].科学出版社. 2003:87～265
[55] Alberto Broggi, Alessandra Fascioli. Artificial Vision in Extreme Environments for Snowcat Tracks Detection. IEEE Transaction on Intelligent Transportation System,September 2002,3(3):162～172
[56]吴宇岚,费旭东,荆仁杰.边界的快速跟踪算法和硬件实现[J].通信学报. 1997.5
[57]章毓晋.图像工程上册:图像处理和分析[M].北京:清华大学出版社. 1999:107～115
[58]孙亚兵.城市CCD图像关键目标区域提取相关技术研究[D].硕士学位论文.青岛:中国海洋大学. 2008
[59]唐亮,谢维信,黄建军,黄敬雄.自适应模糊Hough变换[J].电子学报. 2004,32(6):946～949
[60]陈燕新,戚飞虎.利用梯度方向信息的随机Hough变换[J].红外与毫米波学报. 1998,17(5):375～380