Intelligent Traffic Light Control Algorithm Based on Machine Vision
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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