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Surface defect detection : A feature-based transfer learning approach

Surface defect detection is critical for maintaining product quality in manufacturing. In this work, we apply a feature-based transfer learning approach for surface defect classification on the NEU surface defect database. The database contains defects across 6 categories captured under various cond...

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Main Authors:	Abdul Majeed, Anwar P. P., Muhammad Amirul, Abdullah, Ahmad Fakhri, Ab Nasir, Mohd Azraai, Mohd Razman, Chen, Wei, Yap, Eng Hwa
Format:	Conference or Workshop Item
Language:	English
Published:	Institute of Physics 2024
Subjects:	QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) TA Engineering (General). Civil engineering (General) TJ Mechanical engineering and machinery
Online Access:	http://umpir.ump.edu.my/id/eprint/41728/1/Surface%20defect%20detection_A%20feature-based%20transfer%20learning%20approach.pdf http://umpir.ump.edu.my/id/eprint/41728/ https://doi.org/10.1088/1742-6596/2762/1/012088
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spelling	my.ump.umpir.417282024-07-31T03:30:33Z http://umpir.ump.edu.my/id/eprint/41728/ Surface defect detection : A feature-based transfer learning approach Abdul Majeed, Anwar P. P. Muhammad Amirul, Abdullah Ahmad Fakhri, Ab Nasir Mohd Azraai, Mohd Razman Chen, Wei Yap, Eng Hwa QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) TA Engineering (General). Civil engineering (General) TJ Mechanical engineering and machinery Surface defect detection is critical for maintaining product quality in manufacturing. In this work, we apply a feature-based transfer learning approach for surface defect classification on the NEU surface defect database. The database contains defects across 6 categories captured under various conditions. We utilised two pretrained convolutional neural network (CNN) architectures - VGG16 and InceptionV3 - by removing the final classification layer and using the CNN as a fixed feature extractor. The output feature vectors were classified using a logistic regression (LR) model. The data was split into train, validation, and test sets with a 70:15:15 ratio. The VGG16-LR model achieved classification accuracy (CA) of 100%, 98%, and 99% for the train, validation, and test sets respectively. The InceptionV3-LR model attained CA of 100%, 91%, and 92% for train, validation, and test. The results demonstrate the effectiveness of transfer learning with CNN feature extraction for surface defect detection on challenging multi-category industrial datasets. Further work includes tuning hyperparameters and evaluating additional architectures. Institute of Physics 2024 Conference or Workshop Item PeerReviewed pdf en cc_by_4 http://umpir.ump.edu.my/id/eprint/41728/1/Surface%20defect%20detection_A%20feature-based%20transfer%20learning%20approach.pdf Abdul Majeed, Anwar P. P. and Muhammad Amirul, Abdullah and Ahmad Fakhri, Ab Nasir and Mohd Azraai, Mohd Razman and Chen, Wei and Yap, Eng Hwa (2024) Surface defect detection : A feature-based transfer learning approach. In: Journal of Physics: Conference Series. 2023 International Symposium on Structural Dynamics of Aerospace, ISSDA 2023 , 9-10 September 2023 , Xi'an. pp. 1-7., 2762 (012088). ISSN 1742-6588 (Published) https://doi.org/10.1088/1742-6596/2762/1/012088
institution	Universiti Malaysia Pahang Al-Sultan Abdullah
building	UMPSA Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Malaysia Pahang Al-Sultan Abdullah
content_source	UMPSA Institutional Repository
url_provider	http://umpir.ump.edu.my/
language	English
topic	QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) TA Engineering (General). Civil engineering (General) TJ Mechanical engineering and machinery
spellingShingle	QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) TA Engineering (General). Civil engineering (General) TJ Mechanical engineering and machinery Abdul Majeed, Anwar P. P. Muhammad Amirul, Abdullah Ahmad Fakhri, Ab Nasir Mohd Azraai, Mohd Razman Chen, Wei Yap, Eng Hwa Surface defect detection : A feature-based transfer learning approach
description	Surface defect detection is critical for maintaining product quality in manufacturing. In this work, we apply a feature-based transfer learning approach for surface defect classification on the NEU surface defect database. The database contains defects across 6 categories captured under various conditions. We utilised two pretrained convolutional neural network (CNN) architectures - VGG16 and InceptionV3 - by removing the final classification layer and using the CNN as a fixed feature extractor. The output feature vectors were classified using a logistic regression (LR) model. The data was split into train, validation, and test sets with a 70:15:15 ratio. The VGG16-LR model achieved classification accuracy (CA) of 100%, 98%, and 99% for the train, validation, and test sets respectively. The InceptionV3-LR model attained CA of 100%, 91%, and 92% for train, validation, and test. The results demonstrate the effectiveness of transfer learning with CNN feature extraction for surface defect detection on challenging multi-category industrial datasets. Further work includes tuning hyperparameters and evaluating additional architectures.
format	Conference or Workshop Item
author	Abdul Majeed, Anwar P. P. Muhammad Amirul, Abdullah Ahmad Fakhri, Ab Nasir Mohd Azraai, Mohd Razman Chen, Wei Yap, Eng Hwa
author_facet	Abdul Majeed, Anwar P. P. Muhammad Amirul, Abdullah Ahmad Fakhri, Ab Nasir Mohd Azraai, Mohd Razman Chen, Wei Yap, Eng Hwa
author_sort	Abdul Majeed, Anwar P. P.
title	Surface defect detection : A feature-based transfer learning approach
title_short	Surface defect detection : A feature-based transfer learning approach
title_full	Surface defect detection : A feature-based transfer learning approach
title_fullStr	Surface defect detection : A feature-based transfer learning approach
title_full_unstemmed	Surface defect detection : A feature-based transfer learning approach
title_sort	surface defect detection : a feature-based transfer learning approach
publisher	Institute of Physics
publishDate	2024
url	http://umpir.ump.edu.my/id/eprint/41728/1/Surface%20defect%20detection_A%20feature-based%20transfer%20learning%20approach.pdf http://umpir.ump.edu.my/id/eprint/41728/ https://doi.org/10.1088/1742-6596/2762/1/012088
_version_	1822924557922598912
score	13.23243

Surface defect detection : A feature-based transfer learning approach

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