Surface Defect Detection: A feature-based transfer learning approach

Anwar P.P. Abdul Majeed*, Muhammad Amirul Abdullah, Ahmad Fakhri Ahmad, Mohd Azraai Mohd Razman, Wei Chen, Eng Hwa Yap

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review


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.

Original languageEnglish
Article number012088
JournalJournal of Physics: Conference Series
Issue number1
Publication statusPublished - 2024
Event2023 International Symposium on Structural Dynamics of Aerospace, ISSDA 2023 - Xi'an, China
Duration: 9 Sept 202310 Sept 2023


  • Deep Learning
  • Feature Extraction
  • Surface Defect Detection
  • Transfer Learning


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