用AI视觉实时识别车流与异常，让城市交通更安全高效
Revolutionizing Traffic Management with AI-Powered Machine Vision: A Step Toward Smart Cities
- 结合摄像头与深度学习，实现车辆与驾驶行为的实时检测
- 采用YOLOv8/v11模型，车辆检测准确率显著提升
- 融合地理与天气数据，系统可自适应复杂环境
城市快速扩张和车辆拥堵给交通管理与安全带来严峻挑战。本研究探索人工智能与机器视觉技术在交通系统中的变革潜力。通过先进监控摄像头与深度学习算法，提出一套实时检测车辆、交通异常及驾驶行为的系统。系统集成地理空间与气象数据，动态适应环境变化，在多种场景下保持稳健性能。基于YOLOv8和YOLOv11模型，实现了高精度的车辆检测与异常识别，有效优化交通流并提升道路安全。研究成果为智能交通管理提供新方案，助力构建可持续、高效的智慧城市基础设施。
原文摘要 · Abstract (English)
The rapid urbanization of cities and increasing vehicular congestion have posed significant challenges to traffic management and safety. This study explores the transformative potential of artificial intelligence (AI) and machine vision technologies in revolutionizing traffic systems. By leveraging advanced surveillance cameras and deep learning algorithms, this research proposes a system for real-time detection of vehicles, traffic anomalies, and driver behaviors. The system integrates geospatial and weather data to adapt dynamically to environmental conditions, ensuring robust performance in diverse scenarios. Using YOLOv8 and YOLOv11 models, the study achieves high accuracy in vehicle detection and anomaly recognition, optimizing traffic flow and enhancing road safety. These findings contribute to the development of intelligent traffic management solutions and align with the vision of creating smart cities with sustainable and efficient urban infrastructure.
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