Modification of a convolutional neural network for the weave pattern classification

  • Noreen Akram Department of Telecommunications Engineering, NED University of Engineering and Technology, Karachi, Pakistan
  • Rizwan Aslam Butt Department of Telecommunications Engineering, NED University of Engineering and Technology, Karachi, Pakistan
  • Muhammad Amir Qureshi Department of Textile Engineering, NED University of Engineering and Technology, Karachi, Pakistan

Abstract

The fabric quality in textile industry is characterized by the texture (weave pattern) as it plays a vital role for the production and design of best quality fabric. The earlier proposed automated weave identification methods based on image processing techniques are highly dependent on the lighting conditions. The machine learning methods have been reported to show better accuracy. However, they require very large training datasets, very high processing power and computation time. This study proposes improved accuracy with smaller dataset and reduced computation time by proposing a modification of VGG16 model by adding two additional pooling layers. Using evaluation metrics of both models, the modified model results were analysed according to accuracy, balanced accuracy, and F1-score. On the basis of investigational outcomes, a comparison has been performed with earlier work. The results show that the proposed VGG-16 model is capable to achieve state-of-the-art accuracy and avoid unnecessary activation features by freezing the main convolutional base layers. Ultimately, as evidenced by the performance of the modified VGG-16 deep learning model, the proposed method demonstrated improved accuracy. The study results show that the proposed modified VGG16 algorithm is able to recognize the features of provided database with 90% accuracy and F1-Score ranging from 0.8 to1.

Published
Apr 6, 2024
How to Cite
AKRAM, Noreen; BUTT, Rizwan Aslam; QURESHI, Muhammad Amir. Modification of a convolutional neural network for the weave pattern classification. Mehran University Research Journal of Engineering and Technology, [S.l.], v. 43, n. 2, p. 79-90, apr. 2024. ISSN 2413-7219. Available at: <https://publications.muet.edu.pk/index.php/muetrj/article/view/2998>. Date accessed: 24 may 2024. doi: http://dx.doi.org/10.22581/muet1982.2998.
Section
Articles
This is an open Access Article published by Mehran University of Engineering and Technolgy, Jamshoro under CCBY 4.0 International License