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.

翻译：超速行驶是导致道路死亡事故的主要原因，在乌干达等道路安全基础设施有限的发展中国家尤为突出。本研究针对此类地区提出了一种实时智能交通监控系统，利用计算机视觉技术解决车辆检测、车牌识别和速度估计问题。研究使用测速枪、佳能相机和手机采集了丰富的数据集以训练模型。基于YOLOv8的车牌检测实现了97.9%的平均精度均值（mAP）。对于检测车牌的字符识别，CNN模型的字符错误率（CER）为3.85%，而Transformer模型将CER显著降低至1.79%。速度估计采用源和目标感兴趣区域方法，取得了10公里/小时误差范围的良好性能。此外，研究建立了将用户信息与车辆检测数据关联的数据库，通过Africa's Talking API实现了短信自动罚单发送。该系统解决了资源受限环境下的关键交通管理需求，在急需此类干预措施的发展中国家，通过自动化交通执法展现了减少道路事故的潜力。