Material Classification with a Transfer Learning based Deep Model on an imbalanced Dataset using an epochal Deming-Cycle-Methodology
Klaiber, Marco (Aalen University of Applied Sciences)
Kaerger, Sophia (Aalen University of Applied Sciences)
Minsch, Yannik (Aalen University of Applied Sciences)

Data:	2022
Resum:	This work demonstrates that a transfer learning-based deep learning model can perform unambiguous classification based on microscopic images of material surfaces with a high degree of accuracy. A transfer learning-enhanced deep learning model was successfully used in combination with an innovative approach for eliminating noisy data based on automatic selection using pixel sum values, which was refined over different epochs to develop and evaluate an effective model for classifying microscopy images. The deep learning model evaluated achieved 91. 54% accuracy with the dataset used and set new standards with the method applied. In addition, care was taken to achieve a balance between accuracy and robustness with respect to the model. Based on this scientific report, a means of identifying microscopy images could evolve to support material identification, suggesting a potential application in the domain of materials science and engineering.
Drets:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, i la comunicació pública de l'obra, sempre que no sigui amb finalitats comercials, i sempre que es reconegui l'autoria de l'obra original. No es permet la creació d'obres derivades.
Llengua:	Anglès
Document:	Article ; Versió publicada
Matèria:	Material identification ; CNN ; Transfer learning ; Deep learning ; Material science
Publicat a:	ELCVIA. Electronic letters on computer vision and image analysis, Vol. 21 Núm. 1 (2022) , p. 59-77 (Regular Issue) , ISSN 1577-5097

Adreça original: https://elcvia.cvc.uab.cat/article/view/1517
Adreça alternativa: https://raco.cat/index.php/ELCVIA/article/view/980000000998
DOI: 10.5565/rev/elcvia.1517

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