Comparative Analysis of Advanced AI-based Object Detection Models for Pavement Marking Quality Assessment during Daytime
This work addresses pavement marking quality assessment for transportation safety, but it is incremental as it applies existing YOLO models to a specific domain without major methodological innovations.
This paper tackled the problem of assessing pavement marking quality during daytime by applying YOLOv8 variants to image data from New Jersey, finding that YOLOv8n achieved the highest mean Average Precision for good visibility objects while balancing accuracy and computational efficiency.
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.