Deep learning technology for weld defects classification based on transfer learning and activation features
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AuthorAjmi, Chiraz; Zapata Pérez, Juan Francisco; Elferchichi, Sabra; Zaafouri, Abderrahmen; Laabidi, Kaouther
Research GroupDesarrollo de sistemas y circuitos electrónicos y microelectrónicos
Knowledge AreaIngeniería TelemáticaElectrónica
SponsorsThis work has been partially funded by the Spanish Government through Project RTI2018-097088-B-C33 (MINECO/FEDER, UE).
Realizado en/conUniversity of Tunis; University of Jeddah
PublisherHindawi Publishing Corporation
Bibliographic CitationAjmi, C., Zapata, J., Elferchichi, S., Zaafouri,A., Laabidi, K. Deep Learning Technology for Weld Defects Classification Based on Transfer Learning and Activation Features. En Advances in Materials Science and Engineering, 2020, 16 pages https://doi.org/10.1155/2020/1574350
KeywordsWeld Defect Images
Weld defects detection using X-ray images is an effective method of nondestructive testing. Conventionally, this work is based on qualified human experts, although it requires their personal intervention for the extraction and classification of heterogeneity. Many approaches have been done using machine learning (ML) and image processing tools to solve those tasks. Although the detection and classification have been enhanced with regard to the problems of low contrast and poor quality, their result is still unsatisfying. Unlike the previous research based on ML, this paper proposes a novel classification method based on deep learning network. In this work, an original approach based on the use of the pretrained network AlexNet architecture aims at the classification of the shortcomings of welds and the increase of the correct recognition in our dataset. Transfer learning is used as methodology with the pretrained AlexNet model. For deep learning applications, a large amount of X-ray ...
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