Defect detection by a deep learning approach with active IR thermography-Reference-Cited by-同舟云学术

Defect detection by a deep learning approach with active IR thermography

Published:2022-05-17 Issue: Volume: Page:
ISSN:
Container-title:2022 8th International Conference on Control, Decision and Information Technologies (CoDIT)
language:
Short-container-title:

Author:

Guaragnella Giovanna¹,Morelli Davide²,D'Orazio Tiziana³,Galietti Umberto¹,Trentadue Bartolomeo¹,Marani Roberto³

Affiliation:

1. Politecnico di Bari,Department of Mechanics, Mathematics and Management,Bari,Italy

2. Università degli studi di Modena e Reggio Emilia,Modena,Italy

3. CNR-STIIMA,Bari,Italy

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9803856/9803880/09803960.pdf?arnumber=9803960

Reference10 articles.

1. Damage detection on laminated composite materials using several NDT techniques

2. Identification of Defective Areas in Composite Materials by Bivariate EMD Analysis of Ultrasound

3. Image Filtering Algorithms and Techniques: A Review”, International Journal of Advanced Research in Computer Science and Software Engineering;chandel;Bilateral Filtering for Gray and Color Images ” Proceedings of the 1998 IEEE International,1998

4. Damage Investigation in Composite Materials by Means of New Thermal Data Processing Procedures

5. A review of non-destructive testing methods of composite materials

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Memory linked knowledge domain transfer few-shot learning for thermography nondestructive evaluation system;Infrared Physics & Technology;2023-08

2. Curves-Based Similarity Method (CBSM) for Defect Depth Quantization;IEEE Transactions on Instrumentation and Measurement;2023