一种基于OpenCV的车道线检测方法
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摘要
车道线检测是自动驾驶系统和高级驾驶辅助系统的重要组成部分,为车辆提供自身的位置信息。为了提高检测系统的准确性和鲁棒性,本文提出了一种基于OpenCV实现的车道线检测方法,首先,针对摄像机采集图像产生的失真和畸变问题,利用黑白格子标定板对原始图像进行了标定,获得无畸变图像,然后对无畸变的RGB图像分别进行灰度化、平滑滤波、canny边缘检测、感兴趣区域的获取和霍夫变换等过程检测车道线。实验结果表明,该方法能够有效地解决图像中的光线明暗问题,车道线检测地准确率高达92. 49%,具有较高地准确性和鲁棒性,满足自动驾驶系统中对检测地实时性要求,在自动紧急制动系统(AEB)上具有较高地实用价值。
The lane line detection is an important part of the autopilot system and advanced driver assistance systems,providing the vehicle with its own location information. In order to improve the accuracy and robustness of the detection system,this paper proposes a lane line detection method based on OpenCV. First,the original image was calibrated to obtain an undistorted image,using a black and white grid calibration plate for the distortion problems generated by the camera acquisition images. And then these undistorted RGB images were processed with graying,smooth filtering,canny edge detecting,extracting the region of interest,and Hough transformation to detect lane lines. The experimental results show that this method can effectively solve the problem of light in the images. The detection accuracy of the lane line is as high as 92. 49%. It has high accuracy and robustness,and meets the real-time requirements of the detection in automatic driving systems with a high practical value.
The lane line detection is an important part of the autopilot system and advanced driver assistance systems,providing the vehicle with its own location information. In order to improve the accuracy and robustness of the detection system,this paper proposes a lane line detection method based on OpenCV. First,the original image was calibrated to obtain an undistorted image,using a black and white grid calibration plate for the distortion problems generated by the camera acquisition images. And then these undistorted RGB images were processed with graying,smooth filtering,canny edge detecting,extracting the region of interest,and Hough transformation to detect lane lines. The experimental results show that this method can effectively solve the problem of light in the images. The detection accuracy of the lane line is as high as 92. 49%. It has high accuracy and robustness,and meets the real-time requirements of the detection in automatic driving systems with a high practical value.
引文
[1] BERLIN T,ROJO D,ROJAS D,et al. Spirit of Berlin:An Autonomous Car for the DARPA Urban Challenge Hardware and Software Architecture[J]. Technical Semifinalist Paper of DARPA Urban Challenge,2007,12(02):1-25.
[2]王芳,陈超,黄见曦.无人驾驶汽车研究综述[J].中国水运月刊,2016,16(12):126-128.
[3] SAWANO H,OKADA M. A Road Extraction Method by an Active Contour Model with Inertia and Differential Features[J]. IEICE-Transactions on Information and Systems,2006,E89-D(7):2257-2267.
[4]彭红,肖进胜,程显,等.基于扩展卡尔曼滤波器的车道线检测算法[J].光电子·激光,2015,(3):567-574.
[5]陈静思,张爱军.基于道路特征的车道线检测方法综述[J].中国科技纵横,2017,(8):21-23.
[6] HE Y,WANG H,ZHANG B. Color-based road detection in urban traffic scenes[J]. IEEE Transactions on Intelligent Transportation Systems,2004,5(4):309-318.
[7] GRAOVAC S,GOMA A. Detection of Road Image Borders Based on Texture Classification[J]. International Journal of Advanced Robotic Systems,2012,9(242):1.
[8]夏茂盛,孟祥磊,宋占伟,等.基于双目视觉的嵌入式三维坐标提取系统[J].吉林大学学报(信息科学版),2011,29(1):61-66.
[9] ABDEL-AZIZ Y,KARARA H,HAUCK M. Direct Linear Transformation from Comparator Coordinates into Object Space Coordinates in Close-Range Photogrammetry[J].Photogrammetric Engineering&Remote Sensing,2015,81(2):103-107.
[10]李跃,汪亚明,黄文清,等.基于Open CV的摄像机标定方法研究[J].浙江理工大学学报(自然科学版),2010,27(3):417-420.
[11] NADERNEJAD E,SHARIFZADEH S,HASSANPOUR H.Edge detection techniques:Evaluations and comparisons[J]. Applied Mathematics Sciences,2008,2(29-32):1507-1520.
[12]罗磊,王培俊,李文涛,等.一种尖轨轮廓无损检测方法研究[J].激光杂志,2018,39(02):34-38.
[13]刘富强,张姗姗,朱文红,等.一种基于视觉的车道线检测与跟踪算法[J].同济大学学报(自然科学版),2010,38(2):223-229.
[14]杨喜宁,段建民,高德芝,等.基于改进Hough变换的车道线检测技术[J].计算机测量与控制,2010,18(2):292-294.
[2]王芳,陈超,黄见曦.无人驾驶汽车研究综述[J].中国水运月刊,2016,16(12):126-128.
[3] SAWANO H,OKADA M. A Road Extraction Method by an Active Contour Model with Inertia and Differential Features[J]. IEICE-Transactions on Information and Systems,2006,E89-D(7):2257-2267.
[4]彭红,肖进胜,程显,等.基于扩展卡尔曼滤波器的车道线检测算法[J].光电子·激光,2015,(3):567-574.
[5]陈静思,张爱军.基于道路特征的车道线检测方法综述[J].中国科技纵横,2017,(8):21-23.
[6] HE Y,WANG H,ZHANG B. Color-based road detection in urban traffic scenes[J]. IEEE Transactions on Intelligent Transportation Systems,2004,5(4):309-318.
[7] GRAOVAC S,GOMA A. Detection of Road Image Borders Based on Texture Classification[J]. International Journal of Advanced Robotic Systems,2012,9(242):1.
[8]夏茂盛,孟祥磊,宋占伟,等.基于双目视觉的嵌入式三维坐标提取系统[J].吉林大学学报(信息科学版),2011,29(1):61-66.
[9] ABDEL-AZIZ Y,KARARA H,HAUCK M. Direct Linear Transformation from Comparator Coordinates into Object Space Coordinates in Close-Range Photogrammetry[J].Photogrammetric Engineering&Remote Sensing,2015,81(2):103-107.
[10]李跃,汪亚明,黄文清,等.基于Open CV的摄像机标定方法研究[J].浙江理工大学学报(自然科学版),2010,27(3):417-420.
[11] NADERNEJAD E,SHARIFZADEH S,HASSANPOUR H.Edge detection techniques:Evaluations and comparisons[J]. Applied Mathematics Sciences,2008,2(29-32):1507-1520.
[12]罗磊,王培俊,李文涛,等.一种尖轨轮廓无损检测方法研究[J].激光杂志,2018,39(02):34-38.
[13]刘富强,张姗姗,朱文红,等.一种基于视觉的车道线检测与跟踪算法[J].同济大学学报(自然科学版),2010,38(2):223-229.
[14]杨喜宁,段建民,高德芝,等.基于改进Hough变换的车道线检测技术[J].计算机测量与控制,2010,18(2):292-294.