arXiv:2606.08033v1 Announce Type: cross Abstract: Cracks are a critical indicator of building health, and early stage identification is fundamental to prevent harmful damages. Advances in deep learning (DL), particularly convolutional neural networks (CNNs), have enabled scalable solutions for automated crack detection. However, CNN performance strongly depends on the availability of large and diverse datasets, which is particularly challenging for complex surfaces such as masonry. Collecting sufficient real data is time-consuming, while publicly available datasets may not be adequate. To addr
The proliferation of deep learning applications like CNNs across various industries necessitates solutions for data scarcity, especially for complex real-world scenarios like infrastructure inspection.
This development addresses a critical bottleneck in deploying AI for essential infrastructure maintenance, potentially leading to more efficient and reliable civil engineering practices.
The reliance on purely real-world data for training advanced AI models in niche applications is reduced, enabling faster development and deployment cycles.
- · AI-driven inspection companies
- · Civil engineering
- · Construction sector
- · Deep learning researchers
Improved and more widespread autonomous defect detection in infrastructure.
Reduced maintenance costs and extended lifespan of critical buildings and bridges through early intervention.
Enhanced resilience of urban and national infrastructure networks against degradation over time.
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