所属单位：

城市发展与现代交通学院

发表刊物：

International Journal of Pavement Engineering

关键字：

Pavement assessment; crack segmentation; deep learning; limited labeled datasets; crack measurement; crack mapping

摘要：

Automated pavement management systems are essential for detecting, and assessing road cracks to provide accurate, timely data for maintenance planning and resource allocation. Traditional methods rely on large labeled datasets to capture diverse crack patterns, while semi-supervised methods, although beneficial, still require substantial amounts of unlabeled data. This dependency poses challenges in real-world applications where both labeled and unlabeled data can be scarce. In such low-data environments, models may struggle to detect faint crack edges and generalize well, which can lead to overfitting and decreased accuracy. Furthermore, accurately capturing crack width variations, critical for detecting irregular widening, remains a challenge. To address these limitations, we propose a teacher-student framework for crack segmentation and severity assessment designed to work effectively with limited labeled data. This framework precisely measures crack width, type, spacing, and severity, which are essential metrics for Pavement Crack Severity Index (PCSI) evaluation and GPS-based mapping. Testing on real-world datasets and through two application studies reveals that our framework outperforms six other models in segmentation accuracy with limited labeled data. Additionally, it accelerates the mapping process by approximately 76.27% compared to conventional methods, providing an efficient, data-driven solution for road pavement monitoring and maintenance.

第一作者：

MOHAMMED MOHAMMED AMEEN ABDULHAMEED

论文类型：

期刊论文

卷号：

27 / 1 / 2604704

ISSN号：

1029-8436

是否译文：

否

发表时间：

2025-12-10