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All Journal International Journal of Electrical and Computer Engineering
Abdessamad Balouki
University Sultan Moulay Slimane
Computer Science & IT Electrical & Electronics Engineering

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Automatic fabric defect detection employing deep learning Aafaf Beljadid; Adil Tannouche; Abdessamad Balouki
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 4: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i4.pp4129-4136

Abstract

A major issue for fabric quality inspection is in the detection of defaults, it has become an extremely challenging goal for the textile industry to minimize costs in both production and quality inspection. The quality inspection is currently done manually by professionals; hence the need for the implementation of a fast, powerful, robust, and intelligent machine vision system in order to achieve high global quality, uniformity, and consistency of fabrics and to increase productivity. Consequently, the automatic inspection control process can improve productivity and enhance product quality. This article describes the approach used in developing a convolutional neural network for identifying fabric defects from input images of fabric surfaces. The proposed neural network is a pre-trained convolutional model ‘DetectNet’, it was adapted to be more efficient to the fabric image feature extraction. The developed model is capable of successfully distinguishing between defective fabric and non-defective with 93% accuracy for the first model and 96% for the second model.
Co-Authors Aafaf Beljadid Adil Tannouche