Road Marking Distress Detection and Assessment Based on UAV Imagery
Yunfan Nie, Wangjie Wu, Jinhuan Shan, Hongxin Peng, Feiyang Guo, Yaohan Liu, Jingjing Xiao
TL;DR
This paper introduces a UAV-based system for detecting and assessing road marking deterioration, improving road safety and maintenance efficiency.
Contribution
The novel contribution is an integrated framework using UAV imagery and advanced computer vision techniques for efficient road marking distress detection.
Findings
The YOLOv8-MEB model achieves over 90% precision and recall for lane marking detection with high processing speed.
A RANSAC-based template matching method restores intact marking shapes with contour correction for construction inaccuracies.
The framework achieves approximately 10% error in distress ratio calculation for nine common road marking types.
Abstract
With the continuous advancement of autonomous driving technology, lane marking-based environment perception has become a critical component of autonomous vehicle systems. However, long-term vehicle loads cause road markings to deteriorate and fade, significantly compromising driving safety. Traditional road marking quality inspection methods are inefficient and struggle to achieve high-performance, convenient detection. To address these challenges, this paper proposes an integrated framework for road marking detection and evaluation using Unmanned Aerial Vehicle (UAV) imagery. The framework comprises three core modules: lightweight data acquisition, efficient marking extraction, and accurate distress assessment. First, optimized UAV flight parameters enable low-cost, highly flexible, and safe data collection. Second, the YOLOv8-MEB model, combined with instance segmentation screening…
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Taxonomy
TopicsInfrastructure Maintenance and Monitoring · Autonomous Vehicle Technology and Safety · Advanced Neural Network Applications