摘要
为了提高交通效率,传统的固定时序交通灯往往无法适应实时交通流量的变化,导致交通拥堵和延误.智能交通灯可以通过实时监测和分析交通状况,采用智能算法和优化策略来动态调整信号灯的配时,从而提高交通效率,减少拥堵,缩短交通流量的延误时间.本论文研究采用的机器视觉和深度学习技术,例如卷积神经网络(CNN)、目标检测算法(如YOLO、SSD)、图像分割方法(如FCN、UNet)等.应用于交通灯控制,以实现自动识别交通信号、预测交通流量、优化信号时长等目标.并根据需求使用flask框架实现可视化观察实时道路情况.
Abstract
In order to improve traffic efficiency,traditional fixed timing traffic lights often cannot adapt to real-time traffic flow changes,leading to traffic congestion and delay.Intelligent traffic lights can dynamically adjust the timing of traffic signals through real-time monitoring and analysis of traffic conditions,using intelligent algorithms and optimization strategies,thereby improving traffic efficiency,reducing congestion,and shortening the delay time of traffic flow.The machine vision and deep learning technologies used in this study,such as convolutional neural networks(CNN),object detection algorithms(such as YOLO,SSD),image segmentation methods(such as FCN,UNet),etc.Applied to traffic light control to achieve goals such as automatic recognition of traffic signals,prediction of traffic flow,and optimization of signal duration.And use the Flask framework to visualize and observe real-time road conditions according to requirements.
国家科技期刊平台
NSTL
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