用YOLO模型自动评估白天道路标线质量，提升交通安全。
Comparative Analysis of Advanced AI-based Object Detection Models for Pavement Marking Quality Assessment during Daytime
- 采用YOLOv8n、m、x三种变体检测道路标线可见度。
- YOLOv8n在良好可见度物体上mAP最高，且跨IoU阈值表现稳定。
- 适合交通工程中需实时、高精度路面检测的场景。
基于深度学习的视觉目标检测在计算机视觉中至关重要，广泛应用于交通工程领域。本文聚焦于利用You Only Look Once (YOLO)模型，在白天条件下检测道路标线质量，通过其先进架构实现精准、实时的道路安全评估。研究使用新泽西州的图像数据，评估了三种YOLOv8变体：YOLOv8m、YOLOv8n和YOLOv8x。模型根据将标线分类为良好、中等和差可见度的预测准确率进行比较。结果显示，YOLOv8n在良好可见度物体上实现了最高的平均精度（mAP），并在不同交并比（IoU）阈值下表现出稳健性能。本研究通过自动化、高精度的方法，提升了道路标线质量评估能力，从而增强交通运输安全性。
原文摘要 · Abstract (English)
Visual object detection utilizing deep learning plays a vital role in computer vision and has extensive applications in transportation engineering. This paper focuses on detecting pavement marking quality during daytime using the You Only Look Once (YOLO) model, leveraging its advanced architectural features to enhance road safety through precise and real-time assessments. Utilizing image data from New Jersey, this study employed three YOLOv8 variants: YOLOv8m, YOLOv8n, and YOLOv8x. The models were evaluated based on their prediction accuracy for classifying pavement markings into good, moderate, and poor visibility categories. The results demonstrated that YOLOv8n provides the best balance between accuracy and computational efficiency, achieving the highest mean Average Precision (mAP) for objects with good visibility and demonstrating robust performance across various Intersections over Union (IoU) thresholds. This research enhances transportation safety by offering an automated and accurate method for evaluating the quality of pavement markings.
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