Automated Road Crack Localization to Guide Highway Maintenance

2026-01-21

AI Summary

This study addresses the escalating challenge of road cracking exacerbated by climate change, calling for efficient and precise pavement maintenance strategies. The authors propose a novel approach that integrates aerial imagery with open-source geographic data from OpenStreetMap to enable high-accuracy automated crack detection through fine-tuning of the YOLOv11 model, achieving F1-scores of 0.84 for positive (cracked) samples and 0.97 for negative (non-cracked) samples. Furthermore, they introduce—for the first time—an interpretable “Swiss Relative Crack Density Index,” which overcomes the limitations of conventional methods reliant on traffic or temperature data. This index effectively identifies high-risk zones such as urban centers and intersections, offering data-driven decision support for nationwide highway maintenance planning.

Technology Category

• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Computer Vision: Segmentation
• Application Domains: Transportation

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

Abstract

Highway networks are crucial for economic prosperity. Climate change-induced temperature fluctuations are exacerbating stress on road pavements, resulting in elevated maintenance costs. This underscores the need for targeted and efficient maintenance strategies. This study investigates the potential of open-source data to guide highway infrastructure maintenance. The proposed framework integrates airborne imagery and OpenStreetMap (OSM) to fine-tune YOLOv11 for highway crack localization. To demonstrate the framework's real-world applicability, a Swiss Relative Highway Crack Density (RHCD) index was calculated to inform nationwide highway maintenance. The crack classification model achieved an F1-score of $0.84$ for the positive class (crack) and $0.97$ for the negative class (no crack). The Swiss RHCD index exhibited weak correlations with Long-term Land Surface Temperature Amplitudes (LT-LST-A) (Pearson's $r\ = -0.05$) and Traffic Volume (TV) (Pearson's $r\ = 0.17$), underlining the added value of this novel index for guiding maintenance over other data. Significantly high RHCD values were observed near urban centers and intersections, providing contextual validation for the predictions. These findings highlight the value of open-source data sharing to drive innovation, ultimately enabling more efficient solutions in the public sector.

Problem

• road crack localization
• highway maintenance
• open-source data
• infrastructure monitoring
• pavement distress

Innovation

• YOLOv11
• open-source data
• road crack localization
• Swiss RHCD index
• automated infrastructure maintenance