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Efficient utilization of deep learning for the detection of fabric defects

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Abstract

A fabric inspection system is a sophisticated computer vision system that detects fabric defects, automatically. In recent years, human visual inspection has traditionally been used to detect fabric defects. However, this trend is inaccurate and may be expensive due to the need for highly-trained personnel. This paper describes a deep-learning-based fabric inspection system for detecting fabric defects instead of the dependence on personnel. To find the Region of Interest (RoI) in fabric images, the system depends on a saliency-based region detection technique to localize the defected areas in fabric images. The fabric images are then classified into defect-free and defective images using a Convolutional Neural Network (CNN). Four convolutional layers and four max-pooling layers are arranged in the suggested model. A fully-connected layer and a Softmax activation function are also used in the classification task. The results of the experiments indicate that the proposed system exceeds some other state-of-the-art systems in terms of both quality and robustness. The proposed system achieves an average accuracy of 95.8%. Hence, it can be used in applications related to fabric industry.

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Data availability

The manuscript does not contain human or animal studies. The database employed in this paper is provided by Industrial Automation Research Laboratory from the Department of Electrical and Electronic Engineering of Hong Kong University. The databases described in this paper are available at https://www.kaggle.com/datasets/rmshashi/fabric-defect-dataset.

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Authors and Affiliations

  1. Department of Mathematics and Computer Science, Faculty of Science, Menoufia University, Shebin El-Koom, 32511, Egypt

    Aya Zahra & Mohamed Amin

  2. Department of Electronics and Electrical Communications Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf, 32952, Egypt

    Fathi E. Abd El-Samie

  3. Faculty of Artificial Intelligence, Menoufia University, Shebin El-Koom, 32511, Egypt

    Mahmoud Emam

  4. Department of Information Technology, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, Riyadh, P.O. Box 84428, 11671, Saudi Arabia

    Fathi E. Abd El-Samie

Authors
  1. Aya Zahra
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  2. Mohamed Amin
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  3. Fathi E. Abd El-Samie
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  4. Mahmoud Emam
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Correspondence to Aya Zahra or Fathi E. Abd El-Samie.

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Zahra, A., Amin, M., El-Samie, F.E.A. et al. Efficient utilization of deep learning for the detection of fabric defects. Neural Comput & Applic 36, 6037–6050 (2024). https://doi.org/10.1007/s00521-023-09137-0

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  • DOI: https://doi.org/10.1007/s00521-023-09137-0

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