Effective road infrastructure management is crucial for modern society. Traditional manual inspection techniques remain constrained by cost, efficiency, and scalability, while camera and laser imaging methods fail to capture subsurface defects critical for long-term structural integrity. This paper introduces ROVAI, an end-to-end framework that integrates high-resolution X-ray computed tomography imaging and advanced AI-driven analytics, aiming to transform road infrastructure inspection technologies. By leveraging the computational power of world-leading supercomputers, Fugaku and Frontier, and SoTA synchrotron facility (Spring-8), ROVAI enables scalable and high-throughput processing of massive 3D tomographic datasets. Our approach overcomes key challenges, such as the high memory requirements of vision models, the lack of labeled training data, and storage I/O bottlenecks. This seamless integration of imaging and AI analytics facilitates automated defect detection, material composition analysis, and lifespan prediction. Experimental results demonstrate the effectiveness of ROVAI in real-world scenarios, setting a new standard for intelligent, data-driven infrastructure management.
@article{arxiv.2505.13955,
title = {Paradigm Shift in Infrastructure Inspection Technology: Leveraging High-performance Imaging and Advanced AI Analytics to Inspect Road Infrastructure},
author = {Du Wu and Enzhi Zhang and Isaac Lyngaas and Xiao Wang and Amir Ziabari and Tao Luo and Peng Chen and Kento Sato and Fumiyoshi Shoji and Takaki Hatsui and Kentaro Uesugi and Akira Seo and Yasuhito Sakai and Toshio Endo and Tetsuya Ishikawa and Satoshi Matsuoka and Mohamed Wahib},
journal= {arXiv preprint arXiv:2505.13955},
year = {2025}
}
Comments
Submitting this work to be considered for the Gordon Bell Award in SC25