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

2025-05-20

AI Summary

Traditional road inspection methods suffer from high costs, low efficiency, and inability to detect subsurface latent defects. To address these challenges, this paper proposes ROVAI—a novel end-to-end framework that pioneers the integration of synchrotron X-ray computed tomography (Spring-8) with exascale supercomputing (Fugaku/Frontier), coupled with a 3D vision Transformer and distributed deep learning. ROVAI enables fully automated defect detection, material composition analysis, and service-life prediction for road infrastructure. It overcomes key bottlenecks in few-shot modeling, GPU memory constraints, and I/O throughput, supporting real-time processing of large-scale 3D volumetric data. Evaluated on real-world road scenarios, ROVAI achieves sub-millimeter resolution for latent distress identification, attaining 98.7% detection accuracy and a 40× throughput improvement over prior approaches. The framework establishes a new paradigm for high-precision, scalable, and intelligent road asset management.

Technology Category

• Computer Vision: Interpretability, Explainability, and Transparency
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Intelligent Robots: Learning & Optimization for ROB

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

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.

Problem

• Overcoming cost and scalability limits in manual road inspections
• Detecting subsurface defects missed by camera/laser imaging
• Addressing AI model memory and data bottlenecks in analysis

Innovation

• Integrates high-resolution X-ray tomography and AI analytics
• Leverages supercomputers for scalable 3D data processing
• Automates defect detection and lifespan prediction