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| Main Authors: | , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2505.13955 |
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| _version_ | 1866916745305915392 |
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| author | Wu, Du Zhang, Enzhi Lyngaas, Isaac Wang, Xiao Ziabari, Amir Luo, Tao Chen, Peng Sato, Kento Shoji, Fumiyoshi Hatsui, Takaki Uesugi, Kentaro Seo, Akira Sakai, Yasuhito Endo, Toshio Ishikawa, Tetsuya Matsuoka, Satoshi Wahib, Mohamed |
| author_facet | Wu, Du Zhang, Enzhi Lyngaas, Isaac Wang, Xiao Ziabari, Amir Luo, Tao Chen, Peng Sato, Kento Shoji, Fumiyoshi Hatsui, Takaki Uesugi, Kentaro Seo, Akira Sakai, Yasuhito Endo, Toshio Ishikawa, Tetsuya Matsuoka, Satoshi Wahib, Mohamed |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_13955 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Paradigm Shift in Infrastructure Inspection Technology: Leveraging High-performance Imaging and Advanced AI Analytics to Inspect Road Infrastructure Wu, Du Zhang, Enzhi Lyngaas, Isaac Wang, Xiao Ziabari, Amir Luo, Tao Chen, Peng Sato, Kento Shoji, Fumiyoshi Hatsui, Takaki Uesugi, Kentaro Seo, Akira Sakai, Yasuhito Endo, Toshio Ishikawa, Tetsuya Matsuoka, Satoshi Wahib, Mohamed Distributed, Parallel, and Cluster Computing 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. |
| title | Paradigm Shift in Infrastructure Inspection Technology: Leveraging High-performance Imaging and Advanced AI Analytics to Inspect Road Infrastructure |
| topic | Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2505.13955 |