A convolutional neural network approach for steel surface defect detection in nuclear facilities

dc.contributor.author	Sinha, Kristy Gourab
dc.contributor.author	Noor, Fayaz
dc.date.accessioned	2024-09-15T19:00:53Z
dc.date.available	2024-09-15T19:00:53Z
dc.date.issued	2024
dc.description.abstract-translated	This research highlights the effectiveness of sophisticated preprocessing techniques and deep learning architectures in the detection of metal surface defects. The detection of surface defects is paramount in both the steel manufacturing and nuclear industries, as it directly affects product quality, production efficiency, and operational safety. The study underscores the importance of model architecture and preprocessing methods in achieving high classification accuracy	en
dc.format	2 s.	cs
dc.format.mimetype	application/pdf
dc.identifier.isbn	978-80-261-1243-3
dc.identifier.uri	http://hdl.handle.net/11025/57457
dc.language.iso	en
dc.language.iso	en	en
dc.publisher	University of West Bohemia	en
dc.rights	© University of West Bohemia	en
dc.rights.access	openAccess	en
dc.subject	poster	cs
dc.subject.translated	poster	en
dc.title	A convolutional neural network approach for steel surface defect detection in nuclear facilities	en
dc.type	konferenční příspěvek	cs
dc.type.status	Peer reviewed	en
dc.type.version	publishedVersion	en
local.files.count	2	*
local.files.size	1404384	*
local.has.files	yes	*

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