Fabric defect classification using transfer learning and deep learning

Aafaf Beljadid, Adil Tannouche, Abdessamad Balouki


The internal inspection of fabrics is one of the most important phases of production in order to achieve high quality standard in the textile industry. Therefore, developing efficient automatic internal control mechanism has been an extremely major area of research. In this paper, the famous architecture GoogLeNet was fine-tuned into two configurations for texture defect classification that was trained on a textile texture database (TILDA). The experimental result, for both configurations, achieved a significant overall accuracy score of 97% for motif and a non-motif-based images and 89% for mixed images. In the results obtained, it was observed that the second model, which updates the last six layers, was more successful than the first one; which updates the last two layers.


Deep learning; Defect detection; Fabric quality; GoogLeNet; Transfer learning

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DOI: http://doi.org/10.11591/ijai.v12.i3.pp1378-1385


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IAES International Journal of Artificial Intelligence (IJ-AI)
ISSN/e-ISSN 2089-4872/2252-8938 
This journal is published by the Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU).

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