Estimation of stripping ratio of road lane markings by in-vehicle smartphone camera and deep learning-based segmentation

Abstract

This study presents a novel method for evaluating the stripping ratio of road lane markings using a two-step deep learning-based semantic segmentation approach. In the first step, a segmentation model identifies the lane area on the road under various conditions. The second step involves using a deep learning-based segmentation model, which processes the collected training data and annotates it using a multi-section binarization method with various data augmentation strategies to distinguish between stripping and non-stripping areas within the lane markings. Unlike traditional methods that rely heavily on manual inspection or low-robustness image processing techniques, this approach leverages smartphone cameras mounted on moving vehicles to capture and automatically analyze the stripping ratio of lane markings with high accuracy across entire road segments. The results demonstrate a high correlation (R² = 0.9827) with manual evaluations, highlighting the potential of this technique to significantly reduce labor-intensive assessments. The efficiency and effectiveness of this method could revolutionize road maintenance by providing reliable, rapid, and cost-effective assessments of road markings.