摘要
为了解决传统车辆检测实时性差和摄像头获取信息单一的问题,提出了一种基于改进YOLO_v2模型的车辆实时检测算法。基于YOLO_v2网络结构建立车辆检测模型,证明了YOLO_v2算法在车辆检测方面准确率高、实时性好。对YOLO_v2算法进行改进,使改进后的算法能对采集到的车载视频信息进行多维度判断:判断图片中是否有车辆及车辆在图片中的位置,判断被检测车辆与摄像头的相对方位及运动趋势,判断被检测车辆对自身车辆的危险程度。实验结果表明,改进后的模型在车载视频上取得了良好的检测效果,解决了车载视频中车辆检测实时性低的问题,并将传统基于视觉的车辆检测从单一维度检测扩展到了多维度检测。
In order to solve the problems of poor real-time detection and single acquisition informa-tion from vehicle-mounted camera,a real-time vehicle detection algorithm was proposed based on im-proved YOLO_v2 model.Based on the YOLO_v2 network structure,a vehicle detection model was es-tablished,which proved that the YOLO_v2 algorithm had high accuracy and good real-time performance in vehicle detections.And the YOLO_v2 algorithm was improved so that the improved one might per-form multi-dimensional judgments on the vehicle-mounted video informations:judging whether there was a vehicle and the vehicle positions in the pictures,judging the relative positions to camera and the movement trend of the detected vehicles,judging the danger degree of the detected vehicles to the own vehicle.The experimental results show that the improved model achieves good detection effectiveness on vehicle-mounted video,solves the problems of low real-time vehicle detections in vehicle-mounted vid-eo,extends the traditional vision-based vehicle detections from single dimensional detections to multi-di-mensional detections.
作者
黎洲
黄妙华
LI Zhou;HUANG Miaohua(Hubei Key Laboratory of Advanced Technology for Automotive Components,Wuhan University of Technology,Wuhan,430070;Hubei Collaborative Innovation Center for Automotive Components Technology,Wuhan,430070;School of Automotive Engineering,Wuhan University of Technology,Wuhan,430070)
出处
《中国机械工程》
EI
CAS
CSCD
北大核心
2018年第15期1869-1874,共6页
China Mechanical Engineering
基金
国家科技支撑计划资助项目(2015BAG08B0)
作者简介
黎洲,男,1994年生,硕士研究生。研究方向为机器视觉、智能辅助驾驶。E-mail:764616961@qq.com。;黄妙华(通信作者),女,1962年生,教授、博士研究生导师。研究方向为大数据、智能辅助驾驶。发表论文20余篇。E-mail:mh_huang@163.com。
