Computer Vision-Assisted Evaluation of Asphalt Pavement Cracking Based on Aggregate Distribution and Microstructural Characteristics

Oliwier Danieluk; Sonya Webb; Naoise Condon

Authors

Oliwier Danieluk Department of Computer Science, Binghamton University, Binghamton, NY, USA. Author
Sonya Webb Department of Computer Science, University of North Texas, Denton, TX, USA. Author
Naoise Condon Department of Electrical Engineering and Computer Science, University of Kansas, Lawrence, KS, USA. Author

Keywords:

computer vision; asphalt pavement; crack detection; aggregate distribution; microstructure; infrastructure governance; sustainability; robustness

Abstract

Asphalt pavement cracking remains one of the most consequential indicators of infrastructure deterioration, yet conventional visual inspection often treats cracks as surface symptoms rather than expressions of deeper material and structural conditions. This paper examines computer vision-assisted evaluation of asphalt pavement cracking through the lens of aggregate distribution and microstructural characteristics, arguing that more reliable assessment requires coupling image-based crack detection with meso-scale interpretation of mixture structure. The discussion positions computer vision not merely as a replacement for human inspection but as a socio-technical infrastructure that connects sensing, data processing, asset management, governance, and sustainability. It reviews architectures ranging from handcrafted feature extraction to deep segmentation and three-dimensional surface analysis, while emphasizing trade-offs in latency, bandwidth, privacy, robustness, and cost. The paper further explores how aggregate morphology, orientation, contact, air void distribution, and interfacial conditions influence crack initiation and propagation, and how these properties can be inferred or approximated from pavement imagery. Deployment challenges are analyzed in relation to edge and cloud computation, institutional procurement, data standards, fairness across geographic regions, and lifecycle policy. The central claim is that technically accurate crack detection is insufficient unless embedded within governance structures that address data quality, explainability, maintenance prioritization, and equitable infrastructure investment. Future directions include multimodal sensing, uncertainty-aware models, federated learning, digital twins, and policy frameworks that treat pavement evaluation as part of a broader resilient infrastructure system.

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Computer Vision-Assisted Evaluation of Asphalt Pavement Cracking Based on Aggregate Distribution and Microstructural Characteristics

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