A deep learning framework based on improved self‐supervised learning for ground‐penetrating radar tunnel lining inspection

It is not practical to obtain a large number of labeled data to train a supervised learning network in tunnel lining nondestructive testing with ground‐penetrating radar (GPR). To decrease the dependence of supervised learning on the number of labeled data, an improved self‐supervised learning algor...

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Veröffentlicht in:	Computer-aided civil and infrastructure engineering 2024-03, Vol.39 (6), p.814-833
Hauptverfasser:	Huang, Jian, Yang, Xi, Zhou, Feng, Li, Xiaofeng, Zhou, Bin, Lu, Song, Ivashov, Sergey, Giannakis, Iraklis, Kong, Fannian, Slob, Evert
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Artificial neural networks Deep learning Estimation Feature extraction Ground penetrating radar Inspection Machine learning Nondestructive testing Supervised learning Thickness Tunnel linings Workflow
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