基于树莓派的车辆识别与车流监测系统的设计

Design of Vehicle Identification and Traffic Flow Monitoring System Based on Raspberry PI

为实现交通路段的车流监测与交通灯智能控制,搭建了一套基于树莓派的车辆识别与车流监测系统。系统硬件设计借助嵌入式开发平台,利用摄像头采集路口车辆的图片信息,使用Zig Bee技术实现无线数据传输,采用树莓派主控模块完成图像数据分析与实时学习。系统软件设计综合应用了OpenCV、TensorFlow神经网络模型和Keras深度学习库等技术,采用图像处理技术对车辆数据进行检测、跟踪、分类,实现车辆识别。实验结果表明,系统可准确识别车流量,并能根据路口实际车流量对各方向交通灯时长进行合理控制和分配。

In order to realize traffic flow monitoring and intelligent control of traffic light,a vehicle identification and traffic flow monitoring system based on raspberry PI is built.With the help of the embedded development platform,the system hardware design utilizes the camera to collect road vehicle image information,adopts Zig Bee technology to implement the wireless data transmission,and uses raspberry PI master control module to complete image data analysis and real-time learning.The system software design integrates OpenCV,TensorFlow neural network model,Keras deep learning library and other technologies,and adopts image processing technology to detect,track and classify vehicle data to realize vehicle identification.The experimental results show that the system can accurately identify the traffic flow,reasonably control and allocate the traffic lights time in each direction according to the actual traffic flow at the intersection.