Authors :
Vira Fitriza Fadli; Iwa Ovyawan Herlistiono
Volume/Issue :
Volume 5 - 2020, Issue 7 - July
Google Scholar :
http://bitly.ws/9nMw
Scribd :
https://bit.ly/30Ep8BS
DOI :
10.38124/IJISRT20JUL240
Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.
Abstract :
Steel defects are a frequent problem in steel
companies. Proper quality control can reduce quality
problems arising from steel defects. Nowadays, steel
defects can detect by automation methods that utilize
certain algorithms. Deep learning can help the steel
defect detection algorithm become more sophisticated.
In this study, we use deep learning CNN with Xception
architecture to detect steel defects from images taken
from high-frequency and high-resolution cameras.
There are two techniques used, and both produce
respectively 0.94% and 0.85% accuracy. The Xception
architecture used in this case shows optimal and stable
performance in the process and its results.
Keywords :
Defect Detection, Steel Defect, Deep Learning, Xception.
Steel defects are a frequent problem in steel
companies. Proper quality control can reduce quality
problems arising from steel defects. Nowadays, steel
defects can detect by automation methods that utilize
certain algorithms. Deep learning can help the steel
defect detection algorithm become more sophisticated.
In this study, we use deep learning CNN with Xception
architecture to detect steel defects from images taken
from high-frequency and high-resolution cameras.
There are two techniques used, and both produce
respectively 0.94% and 0.85% accuracy. The Xception
architecture used in this case shows optimal and stable
performance in the process and its results.
Keywords :
Defect Detection, Steel Defect, Deep Learning, Xception.
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