用视觉技术实现低成本实时交通监控，支持查车速、认车牌、自动开罚单。
Intelligent Traffic Surveillance for Real-Time Vehicle Detection, License Plate Recognition, and Speed Estimation
- 基于YOLOv8和CNN/Transformer模型，实现高精度车牌检测与识别。
- 车牌字符错误率低至1.79%，速度估计误差控制在10km/h以内。
- 专为资源有限地区设计，可联动短信系统自动发罚单，适合发展中国家推广。
超速是导致道路死亡事故的主要原因，尤其在乌干达等基础设施薄弱的发展中国家。本研究提出一种面向此类地区的实时智能交通监控系统，利用计算机视觉技术实现车辆检测、车牌识别和速度估计。研究使用测速仪、佳能相机及手机采集丰富数据集以训练模型。基于YOLOv8的车牌检测达到97.9%的平均精度（mAP）；针对已检测车牌的字符识别，CNN模型字符错误率（CER）为3.85%，而Transformer模型将误差显著降低至1.79%。速度估计通过源与目标感兴趣区域实现，误差控制在10 km/h以内。此外，构建数据库关联用户信息与车辆检测数据，借助Africa's Talking API实现短信自动开罚单。该系统有效应对资源受限环境下的交通管理需求，有望通过自动化执法减少交通事故，在亟需干预的发展中国家具有重要应用潜力。
原文摘要 · Abstract (English)
Speeding is a major contributor to road fatalities, particularly in developing countries such as Uganda, where road safety infrastructure is limited. This study proposes a real-time intelligent traffic surveillance system tailored to such regions, using computer vision techniques to address vehicle detection, license plate recognition, and speed estimation. The study collected a rich dataset using a speed gun, a Canon Camera, and a mobile phone to train the models. License plate detection using YOLOv8 achieved a mean average precision (mAP) of 97.9%. For character recognition of the detected license plate, the CNN model got a character error rate (CER) of 3.85%, while the transformer model significantly reduced the CER to 1.79%. Speed estimation used source and target regions of interest, yielding a good performance of 10 km/h margin of error. Additionally, a database was established to correlate user information with vehicle detection data, enabling automated ticket issuance via SMS via Africa's Talking API. This system addresses critical traffic management needs in resource-constrained environments and shows potential to reduce road accidents through automated traffic enforcement in developing countries where such interventions are urgently needed.
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