Paradigm Shift in Infrastructure Inspection Technology: Leveraging High-performance Imaging and Advanced AI Analytics to Inspect Road Infrastructure

TL;DR

This paper presents ROVAI, a novel AI-powered framework utilizing high-resolution X-ray imaging and supercomputing to automate and enhance road infrastructure inspection, addressing limitations of traditional methods and enabling detailed subsurface defect analysis.

Contribution

Introduces ROVAI, an innovative end-to-end system combining advanced imaging and AI analytics with supercomputing to improve infrastructure inspection capabilities.

Findings

Effective automated defect detection demonstrated

High-throughput processing of large 3D datasets achieved

Enhanced material and lifespan analysis validated in real-world scenarios

Abstract

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.