Comparative Analysis of Advanced AI-based Object Detection Models for Pavement Marking Quality Assessment during Daytime

2025-03-14

AI Summary

This study addresses the problem of automated daytime road marking visibility quality assessment. We propose a classification-based detection method leveraging the YOLOv8 model family, specifically designed for multi-class (excellent/medium/poor) visibility grading on real-world imagery collected in New Jersey. To our knowledge, this work presents the first systematic comparative analysis of YOLOv8n, YOLOv8m, and YOLOv8x in terms of accuracy–speed trade-offs. Experimental results show that YOLOv8n achieves the optimal balance between mAP and inference speed, attaining the highest detection accuracy for excellent-quality markings and demonstrating superior robustness across varying IoU thresholds. The method integrates transfer learning with IoU-robust evaluation, enabling end-to-end, real-time, deployable visual assessment. Our contribution is a lightweight, reliable, and production-ready solution for intelligent road safety inspection.

Technology Category

• Computer Vision: Object Detection & Categorization
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Learning to Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Security and Privacy: Large-scale security measurements
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

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.

Problem

• Assessing pavement marking quality using AI-based object detection.
• Comparing YOLOv8 variants for accuracy and computational efficiency.
• Enhancing road safety through real-time pavement marking evaluation.

Innovation

• Utilizes YOLOv8 variants for pavement marking detection
• Evaluates models based on visibility classification accuracy
• YOLOv8n balances accuracy and computational efficiency best