Pattern Deep Region Learning for Crack Detection in Thermography Diagnosis System
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Title
Pattern Deep Region Learning for Crack Detection in Thermography Diagnosis System
Authors
Keywords
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Journal
Metals
Volume 8, Issue 8, Pages 612
Publisher
MDPI AG
Online
2018-08-07
DOI
10.3390/met8080612
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- (2014) Davide Palumbo et al. MECCANICA
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- (2007) M. Ravan et al. IET Science Measurement & Technology
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