基于深度学习的交通图像识别与智能交通管理技术研究

Traffic Image Recognition and Intelligent Traffic Management Based on Deep Learning Research

在我国城镇化快速发展的背景下,城市道路交通问题日趋严重,对道路交通管理的智能化要求也越来越高。深度学习是人工智能的重要研究方向,在道路图像识别、智能交通管理等方面具有广阔的应用前景。文中探讨了利用深度学习的交通图像识别技术及智能交通管理系统的开发。通过介绍卷积神经网络(CNN)和循环神经网络(RNN)等深度学习模型在交通标志识别、车辆检测与分类中的应用,分析AI识别技术在交通事件检测中的关键作用,并讨论了智能交通管理系统如何通过AI技术实现交通信号控制和流量优化。

Under the background of the rapid development of urbanization in our country,urban road traffic problems are becoming more and more serious,and the intelligent requirements for road traffic management are also getting higher and higher.Deep learning is an important research direction of artificial intelligence,and has broad application prospects in road image recognition and intelligent traffic management.This paper discusses the development of traffic image recognition technology and intelligent traffic management system using deep learning.By introducing the application of deep learning models such as convolutional neural networks(CNN)and recurrent neural networks(RNN)in traffic sign recognition,vehicle detection and classification,analyzing the key role of AI recognition technology in traffic incident detection,and discussing how the intelligent traffic management system uses AI technology implementation of traffic signal control and flow optimization.