摘要
车速检测是城市交通体系中车辆运行安全的重要环节,对维持城市交通安全至关重要。针对现有的多种交通车辆测速方法存在高成本、易受外界条件影响、安装区域限制等问题,本文提出一种成本较低、灵活性高的基于视频图像的车辆识别与测速方法。采用深度学习的方法搭建YOLOv4框架并训练COCO数据集识别车辆,改进识别方法提取识别车辆外接最大矩形框下边界中点的像平面坐标,引入近景摄影测量的方法并对共线方程进行改进,在单摄像机情况下完成对车辆的识别,计算车辆瞬时速度,绘制检测区域内车辆速度曲线,最后采取试验验证方法可行性并进行精度评定。
Speed detection plays a crucial role in ensuring the safe operation of vehicles in urban transportation systems,making it essential for maintaining traffic safety.However,existing methods for measuring vehicle speed suffer from high costs,susceptibility to external conditions,and limitations in installation areas.To address these issues,this paper proposes a low-cost and flexible vehicle recognition and speed measurement method based on video imagery.The approach utilizes deep learning techniques to construct the YOLOv4 framework and train it on the COCO dataset for vehicle identification.The recognition method is improved by extracting the pixel coordinates of the midpoint of the lower boundary of the maximum bounding rectangle encompassing the recognized vehicles.Additionally,a close-range photogrammetry method is introduced,and improvements are made to the collinearity equations to enable vehicle recognition in a single-camera setup.The displacement of vehicles is computed within a fixed time interval,and a velocity curve of vehicles within the monitoring area is plotted.Experimental validation is conducted to assess the feasibility and accuracy of the proposed method.
作者
张劭斌
张志华
ZHANG Shaobin;ZHANG Zhihua(Mapping and geographic information School,Lanzhou Jiaotong University,Lanzhou 730000,China)
出处
《测绘通报》
CSCD
北大核心
2024年第3期19-24,共6页
Bulletin of Surveying and Mapping
基金
中央政府指导地方科技发展计划项目(22ZY1QA005)
国家自然科学基金(42161069)
兰州交通大学项目(201806)
甘肃省重点科技项目(21YF11GA08)
甘肃省科技计划项目(23JRRA870)。
关键词
YOLOv4
近景摄影测量
单镜头
车速检测
YOLOv4
close-range photogrammetry
single lens
vehicle speed detection
