2021 Twelfth International Conference on Ubiquitous and Future Networks (ICUFN) 2021
DOI: 10.1109/icufn49451.2021.9528702
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Metal Defect Classification Using Deep Learning

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Cited by 9 publications
(5 citation statements)
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“…Meanwhile, Deshpande et al [208] proposed a Siamese CNN for one-shot defect recognition on steel surfaces. Prihatno et al [209] employed CNNs to detect defects in steel sheets, achieving 96.00% and 73.00% accuracy in training and testing, respectively. Ibrahim and Tapamo [210] combined a pre-trained VGG16 model as a feature extractor with a newly designed CNN classifier to address the challenges of diversity and similarity among defect types.…”
Section: Deep Learningmentioning
confidence: 99%
“…Meanwhile, Deshpande et al [208] proposed a Siamese CNN for one-shot defect recognition on steel surfaces. Prihatno et al [209] employed CNNs to detect defects in steel sheets, achieving 96.00% and 73.00% accuracy in training and testing, respectively. Ibrahim and Tapamo [210] combined a pre-trained VGG16 model as a feature extractor with a newly designed CNN classifier to address the challenges of diversity and similarity among defect types.…”
Section: Deep Learningmentioning
confidence: 99%
“…The weighted F1 score was initially determined to be 91%, and after utilizing the weighted loss approach, it was determined to be 92%. A deep learning technique for implementing defect detection in smart factory is explained in [20]. Open dataset of steel defects is used for the study.…”
Section: Related Workmentioning
confidence: 99%
“…Target identification of minor imperfections on the steel surface with an average accuracy of 75% utilising the enhanced Faster RCNN to rebuild the network structure 30) . Increasing productivity and cutting production costs by using deep learning algorithms to recognise and categorise steel sheets in smart factories (with 96% accuracy, 95% recall, and 97% precision 31) ).…”
Section: Related Workmentioning
confidence: 99%