摘要
针对车辆检测中使用传统单一传感器的识别效果差、易受干扰等缺点,本文提出一种基于毫米波雷达和机器视觉融合的车辆检测方法。首先利用分层聚类算法对雷达数据进行处理,过滤无效目标;利用改进的YOLO v2算法降低漏检率,提高检测速度;然后运用目标检测交并比和全局最近邻数据关联算法实现多传感器数据融合;最后基于扩展卡尔曼滤波算法进行目标跟踪,而得出最终结果。实车试验结果表明,该方法的车辆识别效果优于单一传感器,且在多种路况下识别效果良好。
Aiming at the defects of poor identification effects and prone to be disturbed when using traditional single sensor in vehicle detection,a vehicle detection method based on the fusion of millimeter wave radar and machine vision is propose in this paper.Firstly,the radar data is processed by using hierarchical clustering algorithm with invalid targets filtered out,and the improved YOLO v2 algorithm is adopted to reduce the missed detection rate and increase the detection speed.Then,the intersection-over-union(IoU)of target detection and the glob-al nearest neighbor data association algorithm are utilized to achieve multi-sensor data fusion.Finally,the extended Kalman filter algorithm is employed for target tracking,with the final result obtained.The results of real vehicle test show that the results of vehicle identification with the method proposed is better than that with single sensor,and has good recognition effects under various road conditions.
作者
张炳力
詹叶辉
潘大巍
程进
宋伟杰
刘文涛
Zhang Bingli;Zhan Yehui;Pan Dawei;Cheng Jin;Song Weijie;Liu Wentao(School of Automobile and Traffic Engineering,Hefei University of Technology,Hefei 230041;Anhui Engineering Laboratory of Intelligent Automobile,Hefei 230009;Hefei Changan Automobile Co.,Ltd.,Hefei 230031)
出处
《汽车工程》
EI
CSCD
北大核心
2021年第4期478-484,共7页
Automotive Engineering
基金
合肥长安汽车企业委托项目(W2019JSKF0220)
安徽省第五批特支计划资助项目和安徽省科技重大专项(18030701199)资助。
