用AI实时检测摩托车头盔、后视镜缺失和车牌，提升交通执法效率。
A Novel AI-Driven System for Real-Time Detection of Mirror Absence, Helmet Non-Compliance, and License Plates Using YOLOv8 and OCR
- 结合YOLOv8与OCR实现多违规行为实时检测。
- 在自建数据集上达到mAP@50 0.843，召回率0.886。
- 适合交警部门部署，可应对复杂光照与角度挑战。
道路安全是全球重大关切，人工执行头盔规定和车辆安全标准耗时且不一致。本文提出一种基于AI的自动化交通违规检测系统，显著提升执法效率与道路安全。系统采用YOLOv8进行目标检测，结合EasyOCR实现车牌识别。基于自建标注图像数据集（经增强以提高多样性）训练，可识别头盔未佩戴、摩托车后视镜缺失（创新点）、提取车辆登记号。通过Streamlit构建界面，支持实时监控与违规记录。先进图像预处理技术提升了复杂条件下的车牌识别效果。评估显示，模型整体精确率为0.9147，召回率为0.886，mAP@50为0.843，mAP@50 95达0.503，表明在严格交并比阈值下仍具强检测能力。本研究展示了实用高效的自动化交通执法方案，并讨论了实际部署考量。
原文摘要 · Abstract (English)
Road safety is a critical global concern, with manual enforcement of helmet laws and vehicle safety standards (e.g., rear-view mirror presence) being resource-intensive and inconsistent. This paper presents an AI-powered system to automate traffic violation detection, significantly enhancing enforcement efficiency and road safety. The system leverages YOLOv8 for robust object detection and EasyOCR for license plate recognition. Trained on a custom dataset of annotated images (augmented for diversity), it identifies helmet non-compliance, the absence of rear-view mirrors on motorcycles, an innovative contribution to automated checks, and extracts vehicle registration numbers. A Streamlit-based interface facilitates real-time monitoring and violation logging. Advanced image preprocessing enhances license plate recognition, particularly under challenging conditions. Based on evaluation results, the model achieves an overall precision of 0.9147, a recall of 0.886, and a mean Average Precision (mAP@50) of 0.843. The mAP@50 95 of 0.503 further indicates strong detection capability under stricter IoU thresholds. This work demonstrates a practical and effective solution for automated traffic rule enforcement, with considerations for real-world deployment discussed.
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