AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
A novel data-centric framework that integrates three complementary foundational vision models to automatically detect and segment cracks in diverse construction materials and demonstrates robust performance across diverse materials and environmental conditions, making it valuable for comprehensive infrastructure monitoring programs.
To address the high complexity of dam crack monitoring workflows involving detection followed by segmentation and the limitations of unmanned aerial vehicle applications, this article first proposes reparameterized crack instance segmentation YOLO (RCIS-YOLO). This model uses YOLOv8s-seg as a baseline, optimizing the n...
Surface crack delineation plays an important role in infrastructure inspection because accurate pixel-level mapping of cracks can support condition assessment, maintenance planning, and automated monitoring of roads and other civil surfaces. However, recent crack segmentation methods often depend on heavy network desig...
Accurate pavement crack detection and quantitative assessment are essential for road infrastructure maintenance. However, existing segmentation methods suffer from limited robustness under complex illumination, insufficient modeling of elongated crack continuity, and a lack of automated pipelines for engineering-grade...
An integrated framework for automated segmentation and management of DIC-based cracks using synthetic data, unsupervised learning, and building information modeling (BIM) is introduced.
Image-based crack analysis is essential for automated infrastructure inspection to reduce the cost, subjectivity, and safety risks of manual surveys. Despite advances in visual crack detection, methods that translate predictions into physically meaningful and engineering-interpretable crack condition information remain...
Crack formation in building sealants allows moisture ingress, accelerates degradation, and reduces building envelope durability. Traditional visual inspections are slow and subjective, underscoring the need for automated, quantitative methods. This study presents an end-to-end deep learning framework for segmenting fin...
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