Contrastive self-supervised representation learning framework for metal surface defect detection

Abstract Automated detection of defects on metal surfaces is crucial for ensuring quality control. However, the scarcity of labeled datasets for emerging target defects poses a significant obstacle. This study proposes a self-supervised representation-learning model that effectively addresses this l...
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Autor*in:	Zabin, Mahe [verfasserIn] Kabir, Anika Nahian Binte Kabir, Muhammad Khubayeeb Choi, Ho-Jin Uddin, Jia

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Metal surface defects Lightweight convolutional encoder Semi-supervised learning Self-supervised learning

Anmerkung:	© The Author(s) 2023

Übergeordnetes Werk:	Enthalten in: Journal of Big Data - Berlin : SpringerOpen, 2014, 10(2023), 1 vom: 26. Sept.
Übergeordnetes Werk:	volume:10 ; year:2023 ; number:1 ; day:26 ; month:09

Links:	Volltext

DOI / URN:	10.1186/s40537-023-00827-z

Katalog-ID:	SPR053213017

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520			\|a Abstract Automated detection of defects on metal surfaces is crucial for ensuring quality control. However, the scarcity of labeled datasets for emerging target defects poses a significant obstacle. This study proposes a self-supervised representation-learning model that effectively addresses this limitation by leveraging both labeled and unlabeled data. The proposed model was developed based on a contrastive learning framework, supported by an augmentation pipeline and a lightweight convolutional encoder. The effectiveness of the proposed approach for representation learning was evaluated using an unlabeled pretraining dataset created from three benchmark datasets. Furthermore, the performance of the proposed model was validated using the NEU metal surface-defect dataset. The results revealed that the proposed method achieved a classification accuracy of 97.78%, even with fewer trainable parameters than the benchmark models. Overall, the proposed model effectively extracted meaningful representations from unlabeled image data and can be employed in downstream tasks for steel defect classification to improve quality control and reduce inspection costs.
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allfields	10.1186/s40537-023-00827-z doi (DE-627)SPR053213017 (SPR)s40537-023-00827-z-e DE-627 ger DE-627 rakwb eng Zabin, Mahe verfasserin aut Contrastive self-supervised representation learning framework for metal surface defect detection 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Abstract Automated detection of defects on metal surfaces is crucial for ensuring quality control. However, the scarcity of labeled datasets for emerging target defects poses a significant obstacle. This study proposes a self-supervised representation-learning model that effectively addresses this limitation by leveraging both labeled and unlabeled data. The proposed model was developed based on a contrastive learning framework, supported by an augmentation pipeline and a lightweight convolutional encoder. The effectiveness of the proposed approach for representation learning was evaluated using an unlabeled pretraining dataset created from three benchmark datasets. Furthermore, the performance of the proposed model was validated using the NEU metal surface-defect dataset. The results revealed that the proposed method achieved a classification accuracy of 97.78%, even with fewer trainable parameters than the benchmark models. Overall, the proposed model effectively extracted meaningful representations from unlabeled image data and can be employed in downstream tasks for steel defect classification to improve quality control and reduce inspection costs. Metal surface defects (dpeaa)DE-He213 Lightweight convolutional encoder (dpeaa)DE-He213 Semi-supervised learning (dpeaa)DE-He213 Self-supervised learning (dpeaa)DE-He213 Kabir, Anika Nahian Binte aut Kabir, Muhammad Khubayeeb aut Choi, Ho-Jin aut Uddin, Jia aut Enthalten in Journal of Big Data Berlin : SpringerOpen, 2014 10(2023), 1 vom: 26. Sept. (DE-627)79213219X (DE-600)2780218-8 2196-1115 nnns volume:10 year:2023 number:1 day:26 month:09 https://dx.doi.org/10.1186/s40537-023-00827-z kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2023 1 26 09
spelling	10.1186/s40537-023-00827-z doi (DE-627)SPR053213017 (SPR)s40537-023-00827-z-e DE-627 ger DE-627 rakwb eng Zabin, Mahe verfasserin aut Contrastive self-supervised representation learning framework for metal surface defect detection 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Abstract Automated detection of defects on metal surfaces is crucial for ensuring quality control. However, the scarcity of labeled datasets for emerging target defects poses a significant obstacle. This study proposes a self-supervised representation-learning model that effectively addresses this limitation by leveraging both labeled and unlabeled data. The proposed model was developed based on a contrastive learning framework, supported by an augmentation pipeline and a lightweight convolutional encoder. The effectiveness of the proposed approach for representation learning was evaluated using an unlabeled pretraining dataset created from three benchmark datasets. Furthermore, the performance of the proposed model was validated using the NEU metal surface-defect dataset. The results revealed that the proposed method achieved a classification accuracy of 97.78%, even with fewer trainable parameters than the benchmark models. Overall, the proposed model effectively extracted meaningful representations from unlabeled image data and can be employed in downstream tasks for steel defect classification to improve quality control and reduce inspection costs. Metal surface defects (dpeaa)DE-He213 Lightweight convolutional encoder (dpeaa)DE-He213 Semi-supervised learning (dpeaa)DE-He213 Self-supervised learning (dpeaa)DE-He213 Kabir, Anika Nahian Binte aut Kabir, Muhammad Khubayeeb aut Choi, Ho-Jin aut Uddin, Jia aut Enthalten in Journal of Big Data Berlin : SpringerOpen, 2014 10(2023), 1 vom: 26. Sept. (DE-627)79213219X (DE-600)2780218-8 2196-1115 nnns volume:10 year:2023 number:1 day:26 month:09 https://dx.doi.org/10.1186/s40537-023-00827-z kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2023 1 26 09
allfields_unstemmed	10.1186/s40537-023-00827-z doi (DE-627)SPR053213017 (SPR)s40537-023-00827-z-e DE-627 ger DE-627 rakwb eng Zabin, Mahe verfasserin aut Contrastive self-supervised representation learning framework for metal surface defect detection 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Abstract Automated detection of defects on metal surfaces is crucial for ensuring quality control. However, the scarcity of labeled datasets for emerging target defects poses a significant obstacle. This study proposes a self-supervised representation-learning model that effectively addresses this limitation by leveraging both labeled and unlabeled data. The proposed model was developed based on a contrastive learning framework, supported by an augmentation pipeline and a lightweight convolutional encoder. The effectiveness of the proposed approach for representation learning was evaluated using an unlabeled pretraining dataset created from three benchmark datasets. Furthermore, the performance of the proposed model was validated using the NEU metal surface-defect dataset. The results revealed that the proposed method achieved a classification accuracy of 97.78%, even with fewer trainable parameters than the benchmark models. Overall, the proposed model effectively extracted meaningful representations from unlabeled image data and can be employed in downstream tasks for steel defect classification to improve quality control and reduce inspection costs. Metal surface defects (dpeaa)DE-He213 Lightweight convolutional encoder (dpeaa)DE-He213 Semi-supervised learning (dpeaa)DE-He213 Self-supervised learning (dpeaa)DE-He213 Kabir, Anika Nahian Binte aut Kabir, Muhammad Khubayeeb aut Choi, Ho-Jin aut Uddin, Jia aut Enthalten in Journal of Big Data Berlin : SpringerOpen, 2014 10(2023), 1 vom: 26. Sept. (DE-627)79213219X (DE-600)2780218-8 2196-1115 nnns volume:10 year:2023 number:1 day:26 month:09 https://dx.doi.org/10.1186/s40537-023-00827-z kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2023 1 26 09
allfieldsGer	10.1186/s40537-023-00827-z doi (DE-627)SPR053213017 (SPR)s40537-023-00827-z-e DE-627 ger DE-627 rakwb eng Zabin, Mahe verfasserin aut Contrastive self-supervised representation learning framework for metal surface defect detection 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Abstract Automated detection of defects on metal surfaces is crucial for ensuring quality control. However, the scarcity of labeled datasets for emerging target defects poses a significant obstacle. This study proposes a self-supervised representation-learning model that effectively addresses this limitation by leveraging both labeled and unlabeled data. The proposed model was developed based on a contrastive learning framework, supported by an augmentation pipeline and a lightweight convolutional encoder. The effectiveness of the proposed approach for representation learning was evaluated using an unlabeled pretraining dataset created from three benchmark datasets. Furthermore, the performance of the proposed model was validated using the NEU metal surface-defect dataset. The results revealed that the proposed method achieved a classification accuracy of 97.78%, even with fewer trainable parameters than the benchmark models. Overall, the proposed model effectively extracted meaningful representations from unlabeled image data and can be employed in downstream tasks for steel defect classification to improve quality control and reduce inspection costs. Metal surface defects (dpeaa)DE-He213 Lightweight convolutional encoder (dpeaa)DE-He213 Semi-supervised learning (dpeaa)DE-He213 Self-supervised learning (dpeaa)DE-He213 Kabir, Anika Nahian Binte aut Kabir, Muhammad Khubayeeb aut Choi, Ho-Jin aut Uddin, Jia aut Enthalten in Journal of Big Data Berlin : SpringerOpen, 2014 10(2023), 1 vom: 26. Sept. (DE-627)79213219X (DE-600)2780218-8 2196-1115 nnns volume:10 year:2023 number:1 day:26 month:09 https://dx.doi.org/10.1186/s40537-023-00827-z kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2023 1 26 09
allfieldsSound	10.1186/s40537-023-00827-z doi (DE-627)SPR053213017 (SPR)s40537-023-00827-z-e DE-627 ger DE-627 rakwb eng Zabin, Mahe verfasserin aut Contrastive self-supervised representation learning framework for metal surface defect detection 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Abstract Automated detection of defects on metal surfaces is crucial for ensuring quality control. However, the scarcity of labeled datasets for emerging target defects poses a significant obstacle. This study proposes a self-supervised representation-learning model that effectively addresses this limitation by leveraging both labeled and unlabeled data. The proposed model was developed based on a contrastive learning framework, supported by an augmentation pipeline and a lightweight convolutional encoder. The effectiveness of the proposed approach for representation learning was evaluated using an unlabeled pretraining dataset created from three benchmark datasets. Furthermore, the performance of the proposed model was validated using the NEU metal surface-defect dataset. The results revealed that the proposed method achieved a classification accuracy of 97.78%, even with fewer trainable parameters than the benchmark models. Overall, the proposed model effectively extracted meaningful representations from unlabeled image data and can be employed in downstream tasks for steel defect classification to improve quality control and reduce inspection costs. Metal surface defects (dpeaa)DE-He213 Lightweight convolutional encoder (dpeaa)DE-He213 Semi-supervised learning (dpeaa)DE-He213 Self-supervised learning (dpeaa)DE-He213 Kabir, Anika Nahian Binte aut Kabir, Muhammad Khubayeeb aut Choi, Ho-Jin aut Uddin, Jia aut Enthalten in Journal of Big Data Berlin : SpringerOpen, 2014 10(2023), 1 vom: 26. Sept. (DE-627)79213219X (DE-600)2780218-8 2196-1115 nnns volume:10 year:2023 number:1 day:26 month:09 https://dx.doi.org/10.1186/s40537-023-00827-z kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2023 1 26 09
language	English
source	Enthalten in Journal of Big Data 10(2023), 1 vom: 26. Sept. volume:10 year:2023 number:1 day:26 month:09
sourceStr	Enthalten in Journal of Big Data 10(2023), 1 vom: 26. Sept. volume:10 year:2023 number:1 day:26 month:09
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Metal surface defects Lightweight convolutional encoder Semi-supervised learning Self-supervised learning
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container_title	Journal of Big Data
authorswithroles_txt_mv	Zabin, Mahe @@aut@@ Kabir, Anika Nahian Binte @@aut@@ Kabir, Muhammad Khubayeeb @@aut@@ Choi, Ho-Jin @@aut@@ Uddin, Jia @@aut@@
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author	Zabin, Mahe
spellingShingle	Zabin, Mahe misc Metal surface defects misc Lightweight convolutional encoder misc Semi-supervised learning misc Self-supervised learning Contrastive self-supervised representation learning framework for metal surface defect detection
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topic_title	Contrastive self-supervised representation learning framework for metal surface defect detection Metal surface defects (dpeaa)DE-He213 Lightweight convolutional encoder (dpeaa)DE-He213 Semi-supervised learning (dpeaa)DE-He213 Self-supervised learning (dpeaa)DE-He213
topic	misc Metal surface defects misc Lightweight convolutional encoder misc Semi-supervised learning misc Self-supervised learning
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title	Contrastive self-supervised representation learning framework for metal surface defect detection
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title_sort	contrastive self-supervised representation learning framework for metal surface defect detection
title_auth	Contrastive self-supervised representation learning framework for metal surface defect detection
abstract	Abstract Automated detection of defects on metal surfaces is crucial for ensuring quality control. However, the scarcity of labeled datasets for emerging target defects poses a significant obstacle. This study proposes a self-supervised representation-learning model that effectively addresses this limitation by leveraging both labeled and unlabeled data. The proposed model was developed based on a contrastive learning framework, supported by an augmentation pipeline and a lightweight convolutional encoder. The effectiveness of the proposed approach for representation learning was evaluated using an unlabeled pretraining dataset created from three benchmark datasets. Furthermore, the performance of the proposed model was validated using the NEU metal surface-defect dataset. The results revealed that the proposed method achieved a classification accuracy of 97.78%, even with fewer trainable parameters than the benchmark models. Overall, the proposed model effectively extracted meaningful representations from unlabeled image data and can be employed in downstream tasks for steel defect classification to improve quality control and reduce inspection costs. © The Author(s) 2023
abstractGer	Abstract Automated detection of defects on metal surfaces is crucial for ensuring quality control. However, the scarcity of labeled datasets for emerging target defects poses a significant obstacle. This study proposes a self-supervised representation-learning model that effectively addresses this limitation by leveraging both labeled and unlabeled data. The proposed model was developed based on a contrastive learning framework, supported by an augmentation pipeline and a lightweight convolutional encoder. The effectiveness of the proposed approach for representation learning was evaluated using an unlabeled pretraining dataset created from three benchmark datasets. Furthermore, the performance of the proposed model was validated using the NEU metal surface-defect dataset. The results revealed that the proposed method achieved a classification accuracy of 97.78%, even with fewer trainable parameters than the benchmark models. Overall, the proposed model effectively extracted meaningful representations from unlabeled image data and can be employed in downstream tasks for steel defect classification to improve quality control and reduce inspection costs. © The Author(s) 2023
abstract_unstemmed	Abstract Automated detection of defects on metal surfaces is crucial for ensuring quality control. However, the scarcity of labeled datasets for emerging target defects poses a significant obstacle. This study proposes a self-supervised representation-learning model that effectively addresses this limitation by leveraging both labeled and unlabeled data. The proposed model was developed based on a contrastive learning framework, supported by an augmentation pipeline and a lightweight convolutional encoder. The effectiveness of the proposed approach for representation learning was evaluated using an unlabeled pretraining dataset created from three benchmark datasets. Furthermore, the performance of the proposed model was validated using the NEU metal surface-defect dataset. The results revealed that the proposed method achieved a classification accuracy of 97.78%, even with fewer trainable parameters than the benchmark models. Overall, the proposed model effectively extracted meaningful representations from unlabeled image data and can be employed in downstream tasks for steel defect classification to improve quality control and reduce inspection costs. © The Author(s) 2023
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title_short	Contrastive self-supervised representation learning framework for metal surface defect detection
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Contrastive self-supervised representation learning framework for metal surface defect detection

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