用单摄像头+YOLOv11+OCR组合，实现高精度不停车收费
Vehicle Detection and Classification for Toll collection using YOLOv11 and Ensemble OCR
- 单摄像头搭配YOLOv11与集成OCR，实现车辆检测与识别
- 车牌识别准确率98.5%，轴数检测达94.2%，OCR置信度99.7%
- 适合智慧交通、智能收费系统开发者参考
传统自动收费系统依赖复杂的硬件配置，安装与维护成本高昂。本文提出一种创新方案，仅需每收费站一个摄像头，结合YOLOv11计算机视觉架构与集成OCR技术，实现高效车辆检测与分类。系统在多种环境下取得0.895的平均精度均值（mAP），车牌识别准确率达98.5%，轴数检测准确率为94.2%，OCR置信度达99.7%。系统集成跨IOU区域的智能车辆追踪、基于空间轮位分布的自动轴数统计，以及扩展的实时监控仪表盘。基于2,500张不同环境下的图像进行充分训练，相比传统系统显著降低硬件需求，同时提升性能。本研究为智能交通系统提供了一种可扩展、高精度的现代收费解决方案，有效提升运营效率与用户体验。
原文摘要 · Abstract (English)
Traditional automated toll collection systems depend on complex hardware configurations, that require huge investments in installation and maintenance. This research paper presents an innovative approach to revolutionize automated toll collection by using a single camera per plaza with the YOLOv11 computer vision architecture combined with an ensemble OCR technique. Our system has achieved a Mean Average Precision (mAP) of 0.895 over a wide range of conditions, demonstrating 98.5% accuracy in license plate recognition, 94.2% accuracy in axle detection, and 99.7% OCR confidence scoring. The architecture incorporates intelligent vehicle tracking across IOU regions, automatic axle counting by way of spatial wheel detection patterns, and real-time monitoring through an extended dashboard interface. Extensive training using 2,500 images under various environmental conditions, our solution shows improved performance while drastically reducing hardware resources compared to conventional systems. This research contributes toward intelligent transportation systems by introducing a scalable, precision-centric solution that improves operational efficiency and user experience in modern toll collections.
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