用YOLOv11实现实时车辆检测，无需云端也能高效计数
Intelligent Traffic Monitoring with YOLOv11: A Case Study in Real-Time Vehicle Detection
- 基于YOLOv11与BoT-SORT/ByteTrack实现端侧多目标追踪
- 在多种场景下计数准确率达66.67%-95.83%，车辆检测F1最高达1.00
- 轻量模型+桌面界面，适合城市交通监控部署与智能交通研究
近年来，人工智能驱动的计算机视觉进步显著提升了监控系统能力。交通监控是其中重要应用，结合深度学习目标检测与计数技术。本文提出一种离线实时交通监控系统，采用预训练的YOLOv11检测器与BoT-SORT/ByteTrack进行多目标跟踪，基于PyTorch/OpenCV实现，并封装于Qt桌面界面。该卷积神经网络流水线可从视频流中高效完成车辆检测与计数，无需依赖云端。在多样场景下，系统计数准确率可达66.67%-95.83%。类别级检测表现优异：汽车精度为0.97-1.00，召回率0.82-1.00；卡车精度1.00，召回率0.70-1.00，对应F1分数分别为0.90-1.00和0.82-1.00。尽管恶劣天气可能影响性能，但在常规条件下结果依然稳健。通过融合轻量模型与可访问的本地化界面，本工作推动了未来智慧城市中AI驱动交通监控系统的现代化发展。
原文摘要 · Abstract (English)
Recent advancements in computer vision, driven by artificial intelligence, have significantly enhanced monitoring systems. One notable application is traffic monitoring, which leverages computer vision alongside deep learning-based object detection and counting. We present an offline, real-time traffic monitoring system that couples a pre-trained YOLOv11 detector with BoT-SORT/ByteTrack for multi-object tracking, implemented in PyTorch/OpenCV and wrapped in a Qt-based desktop UI. The CNN pipeline enables efficient vehicle detection and counting from video streams without cloud dependencies. Across diverse scenes, the system achieves (66.67-95.83%) counting accuracy. Class-wise detection yields high precision (cars: 0.97-1.00; trucks: 1.00) with strong recall (cars: 0.82-1.00; trucks: 0.70-1.00), resulting in F1 scores of (0.90-1.00 for cars and 0.82-1.00 for trucks). While adverse weather conditions may negatively impact this performance, results remain robust in typical conditions. By integrating lightweight models with an accessible, cloud-independent interface, this paper contributes to the modernization and development of future smart cities by showing the capacity of AI-driven traffic monitoring systems.
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