دورية أكاديمية
Contrastive self-supervised representation learning framework for metal surface defect detection
| العنوان: | Contrastive self-supervised representation learning framework for metal surface defect detection |
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| المؤلفون: | Mahe Zabin, Anika Nahian Binte Kabir, Muhammad Khubayeeb Kabir, Ho-Jin Choi, Jia Uddin |
| المصدر: | Journal of Big Data, Vol 10, Iss 1, Pp 1-24 (2023) |
| بيانات النشر: | SpringerOpen, 2023. |
| سنة النشر: | 2023 |
| المجموعة: | LCC:Computer engineering. Computer hardware LCC:Information technology LCC:Electronic computers. Computer science |
| مصطلحات موضوعية: | Metal surface defects, Lightweight convolutional encoder, Semi-supervised learning, Self-supervised learning, Computer engineering. Computer hardware, TK7885-7895, Information technology, T58.5-58.64, Electronic computers. Computer science, QA75.5-76.95 |
| الوصف: | 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. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 2196-1115 |
| العلاقة: | https://doaj.org/toc/2196-1115Test |
| DOI: | 10.1186/s40537-023-00827-z |
| الوصول الحر: | https://doaj.org/article/7614a3da93424dc0b3b58cd7e599c5a3Test |
| رقم الانضمام: | edsdoj.7614a3da93424dc0b3b58cd7e599c5a3 |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 21961115 |
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| DOI: | 10.1186/s40537-023-00827-z |