AI-based recognition for road distressed from GPR measurement using artificial neural networks

Qian Liu; Zhen Liu

doi:10.1117/12.3033782

13 June 2024 AI-based recognition for road distressed from GPR measurement using artificial neural networks

Qian Liu, Zhen Liu

Proceedings Volume 13180, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024); 131805R (2024) https://doi.org/10.1117/12.3033782
Event: International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024), 2024, Guangzhou, China

Abstract

Gprmax is utilized for ground-penetrating radar simulation, enabling to generate imagery delineating diverse road pathologies. The autonomous identification and classification of these road afflictions are performed leveraging a dataset trained within the YOLO framework. Renowned for its efficacy in target detection, YOLO harnesses pre-trained model parameters from extensive databases, thereby achieving commendable recognition outcomes. To ameliorate the protracted training duration, the adoption of the random retreat mechanism is advocated. The methodology elucidated in this investigation attains an average recognition rate of 77.83%, effectively achieving the identification of road disease images and thereby enhancing operational efficiency. Significantly, the proposed approach exhibits promising prospects for application and dissemination within engineering domains.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Qian Liu and Zhen Liu "AI-based recognition for road distressed from GPR measurement using artificial neural networks", Proc. SPIE 13180, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024), 131805R (13 June 2024); https://doi.org/10.1117/12.3033782

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