Automated crack detection on metallic materials with flying-spot thermography using deep learning and progressive training-Reference-Cited by-同舟云学术

Automated crack detection on metallic materials with flying-spot thermography using deep learning and progressive training

Published:2023-10-19 Issue: Volume: Page:1-20
ISSN:1768-6733
Container-title:Quantitative InfraRed Thermography Journal
language:en
Short-container-title:Quantitative InfraRed Thermography Journal

Author:

Helvig Kevin¹^ORCID,Trouvé-Peloux Pauline¹,Gaverina Ludovic²,Abeloos Baptiste¹,Roche Jean-Michel²

Affiliation:

1. DTIS, ONERA, Université Paris-Saclay, Palaiseau, France

2. DMAS, ONERA, Université Paris-Saclay, Châtillon, France

Funder

Agence de l’innovation de Défense

Publisher

Informa UK Limited

Subject

Electrical and Electronic Engineering,Instrumentation

Link

https://www.tandfonline.com/doi/pdf/10.1080/17686733.2023.2266176

Reference43 articles.

1. Infrared Detection of Fatigue Cracks and Other Near-Surface Defects

2. Use of laser spot thermography for the non-destructive imaging of thermal fatigue microcracking of a coated ceramic matrix composite

3. Résolution spatiale de la caméra thermique à source volante

4. Mokhtari Y, Gavérina L, Ibarra-Castanedo C, et al. Comparative study of line scan and flying line active IR thermography operated with a 6-axis robot. In: Proceedings of the 2018 International Conference on Quantitative InfraRed Thermography; Jun 2018; Berlin (Germany): QIRT Council; 2018.

5. Crack imaging by pulsed laser spot thermography

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