An Integrated System for Fine-Grained Crack Identification and Dynamic PCI Assessment of Asphalt Pavements with Geometric Features
Source abstract
Abstract
Asphalt pavement maintenance is critical for road service life and traffic safety, yet conventional crack detection and Pavement Condition Index (PCI) assessment methods suffer from inefficiency and subjectivity. This paper presents an integrated system for intelligent crack recognition and automated PCI evaluation, aiming to bridge the gap between automated identification and intelligent assessment. The system employs an optimized YOLOv11l-seg model for precise crack segmentation and geometric parameter extraction, and introduces a refined PCI model incorporating geometry-based adjustment factors for differentiated scoring. Using unmanned aerial vehicle (UAV) data, a fully automated workflow is established—from image acquisition and stitching to crack detection, PCI calculation, and result visualization. Experimental results demonstrate the accuracy of extracted crack parameters and the superior discriminative capability and engineering rationality of the proposed PCI model over conventional approaches. The generated panoramic condition maps provide intuitive visual support for maintenance decision-making. This research validates the feasibility of a fully auto-mated closed-loop system from detection to evaluation, offering a practical solution for intelligent pavement maintenance.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Infrastructure Maintenance and Monitoring · Asphalt Pavement Performance Evaluation · Railway Engineering and Dynamics
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Mechanics and Civil Engineering, China University of Mining and Technology, Xuzhou 221116, China
Funding
National Natural Science Foundation of China · Fundamental Research Funds for the Central Universities
References (30)
Show source-provided references
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