Novel lane detection algorithm based on multi-feature fusion and windows searching
1. School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing, Jiangsu 210044, China; 2. Faculty of Computer and Software Engineering, Huaiyin Institute of Technology, Huaian, Jiangsu 223001, China
摘要 针对现有车道检测算法准确性和实时性较难平衡的问题，提出了一种基于多特征融合和窗口搜索的新型车道线检测算法.采用多边形填充方法确定车道线的感兴趣区域（region of interest，ROI），融合车道线的颜色、直方图和梯度特征，以消除ROI中的复杂背景.通过单应性变换得到车道线的二值图像，基于其像素密度分布寻找车道线初始位置，以窗口搜索方式提取整个车道线上的所有候选像素点.通过拟合像素点构建车道线数学模型.结果表明：提出的算法具有较高的准确性和实时性，算法对黄色车道线、树木阴影遮挡、光照变化、车道线缺损和地面交通标志干扰具有较好的鲁棒性.
Abstract：To solve the problem that the existing lane detection algorithms were difficult to balance accuracy and real-time performance, a novel lane detection algorithm was proposed based on multi-feature fusion and windows searching. The polygon filling method was used to determine the region of interest (ROI) of the lane lines, and the color, histogram and gradient features of the lane lines were fused to eliminate the complex background in the ROI. The binary image of the lane lines was obtained by homography transformation, and the initial position of the lane lines was determined based on the pixel density distribution. The entire candidate pixel points along the lane line were extracted by the window-based searching method, and a mathematical model of the lane line was constructed by fitting the extracted pixels. The results show that the proposed algorithm has high accuracy and real-time performance, and the algorithm has excellent robustness to various factors of yellow lane lines, tree shade obstruction, illumination changes, missing lane lines and interference from ground traffic signs.
蔡创新1， 邹宇1， 潘志庚1， 刘志彬1， 高尚兵2. 基于多特征融合和窗口搜索的新型车道线检测算法[J]. 江苏大学学报（自然科学版）, 2023, 44(4): 386-391.
CAI Chuangxin1， ZOU Yu1， PAN Zhigeng1， LIU Zhibin1， GAO Shangbing2. Novel lane detection algorithm based on multi-feature fusion and windows searching[J]. Journal of Jiangsu University(Natural Science Eidtion)
, 2023, 44(4): 386-391.
ZHANG Y C, LU Z Q, MA D D, et al. Ripple-GAN: lane line detection with ripple lane line detection network and wasserstein GAN[J]. IEEE Transactions on Intelligent Transportation Systems, 2021,22(3):1532-1542.
CAI Y F, GAO L, CHEN L, et al. Lane detection method based on clustering algorithm[J]. Journal of Jiangsu University (Natural Science Edition), 2017,38(6):621-625. （in Chinese）
YANG J X, FAN Y, XIE C L. Lane detection algorithm based on row distance and particle filter[J]. Journal of Jiangsu University (Natural Science Edition), 2020, 41(2):138-142,198. （in Chinese）
［4］
ZARBAKHT N, ZOU J J. Lane detection under adverse conditions based on dual color space[C]∥2018 Digital Image Computing: Techniques and Applications. Pisca-taway，USA:IEEE,2018:1-5.
［5］
BAILI J, MARZOUGUI M, SBOUI A, et al. Lane departure detection using image processing techniques[C]∥2017 2nd International Conference on Anti-Cyber Crimes. Piscataway，USA:IEEE,2017:238-241.
QIAN J D, CHEN B, QIAN J Y，et al. Fast lane detection algorithm based on region of interest model[J]. Journal of University of Electronic Science and Technology of China, 2018,47(3):356-361.（in Chinese）
［7］
AJMAL A, HOLLITT C, FREAN M, et al. A comparison of RGB and HSV colour spaces for visual attention models[C]∥2018 International Conference on Image and Vision Computing. Piscataway，USA:IEEE,2018:1-6.
［8］
SINGH K B, MAHENDRA T V, KURMVANSHI R S, et al. Image enhancement with the application of local and global enhancement methods for dark images[C]∥2017 International Conference on Innovations in Electronics, Signal Processing and Communication. Pisca-taway，USA:IEEE,2017: 199-202.
［9］
CHEN G F, LIANG Q, ZHONG W T, et al. Homography-based measurement of bridge vibration using UAV and DIC method[J]. Measurement,DOI： 10.1016/j.measurement.2020.108683.
［10］
CANNY J. A computational approach to edge detection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1986,8(6):679-698.
［11］
SEZGIN M, SANKUR B. Survey over image thresholding techniques and quantitative performance evaluation[J]. Journal of Electronic Imaging, 2004,13(1): 146-165.
［12］
LUCAS B D, KANADE T. An iterative image registration technique with an application to stereo vision[C]∥Proceedings of the 7th International Joint Conference on Artificial Intelligence，1981:674-679.
［13］
JIANG L, LI J, AI W. Lane line detection optimization algorithm based on improved hough transform and R-least squares with dual removal[C]∥Proceedings of 2019 IEEE 4th Advanced Information Technology, Electronic and Automation Control Conference. Piscataway，USA:IEEE,2019:186-190.
［14］
MA L Y, HUA C S, HE Y Q, et al. A lane detection technique based on adaptive threshold segmentation of lane gradient image[C]∥2018 4th Annual International Conference on Network and Information Systems for Computers, 2018:182-186.
［15］
ZHUANG B Y, DUAN J M, ZHENG B G, et al. Algorithm research of fast lane detection based on optical flow[J]. Computer Measurement & Control, 2019,27(9): 146-150.
2023, Vol. 44 
