Machine Learning for Quality Control in Traditional Textile Manufacturing


  • Vina Sari Yosephine Institut Teknologi Harapan Bangsa
  • Tabitha Hanna Institut Teknologi Harapan Bangsa
  • Marla Setiawati
  • Ari Setiawan Institut Teknologi Harapan Bangsa



quality control, traditional textile industry, machine learning, labor-intensive, digitalization


This research is centered on the practical implementation of machine learning and computer vision technologies to enhance production quality control within the traditional textile industry. The traditional textile sector, known for labor-intensive practices, has slowly adapted to digital transformation. We present a practical case study from Bandung, Indonesia, to validate the effectiveness of our approach in real-world textile manufacturing. By emphasizing machine learning and computer vision, this research narrows the gap between traditional textile practices and digitalization, offering tailored solutions for manufacturers seeking to excel in today's rapidly changing global market. The findings provide valuable insights into the challenges and opportunities of using machine learning and computer vision for production quality control in traditional textile manufacturing. The machine learning models in the study showed good accuracy, ranging from 75% to 100% under various lighting conditions in real-world textile manufacturing environments, confirming their suitability for practical quality control applications.

Author Biographies

Vina Sari Yosephine, Institut Teknologi Harapan Bangsa

Teknik Industri

Tabitha Hanna, Institut Teknologi Harapan Bangsa

Teknik Industri

Ari Setiawan, Institut Teknologi Harapan Bangsa

Teknik Industri


Amaral, A. and Peças, P. (2021). SMEs and Industry 4.0: Two Case Studies of Digitalization for a Smoother Integration. Computers in Industry, 125, 103333.

Dutta, G. et al. (2021). Digitalization Priorities of Quality Control Processes for SMEs: A Conceptual Study in Perspective of Industry 4.0 Adoption. Journal of Intelligent Manufacturing, 32(6), 1679–1698.

Galata, D.L. et al. (2021). Applications of Machine Vision in Pharmaceutical Technology: A Review. European Journal of Pharmaceutical Sciences, 159, 105717.

Javaid, M. et al. (2022). Exploring Impact and Features of Machine Vision for Progressive Industry 4.0 Culture. Sensors International, 3, 100132.

Kim, J.-C. and Moon, I.-Y. (2020). A Study on Smart Factory Construction Method for Efficient Production Management in Sewing Industry. Journal of Information and Communication Convergence Engineering, 18(1), 61–68.

Kim, J.-O., Traore, M.K. and Warfield, C. (2006). The Textile and Apparel Industry in Developing Countries. Textile Progress, 38(3), 1–64.

Konstantinidis, F.K., Mouroutsos, S.G. and Gasteratos, A. (2021). The Role of Machine Vision in Industry 4.0: An Automotive Manufacturing Perspective. Accessed from: [25 September 2023].

Koulali, I. and Eskil, M.T. (2021). Unsupervised Textile Defect Detection Using Convolutional Neural Networks. Applied Soft Computing, 113, 107913.

Lee, S.-E., Ju, N. and Lee, K.-H. (2021). Visioning the Future of Smart Fashion Factories Based on Media Big Data Analysis. Applied Sciences, 11(16), 7549.

Ouyang, W. et al. (2019). Fabric Defect Detection Using Activation Layer Embedded Convolutional Neural Network. IEEE Access, 7, 70130–70140.

Saberironaghi, A., Ren, J. and El-Gindy, M. (2023). Defect Detection Methods for Industrial Products Using Deep Learning Techniques: A Review. Algorithms, 16(2), 95.

Simonyan, K. and Zisserman, A. (2015). Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv preprint arXiv:1409.1556.

Wang, X., Wu, G. and Zhong, Y. (2019). Fabric Identification Using Convolutional Neural Network. Artificial Intelligence on Fashion and Textiles: Proceedings of the Artificial Intelligence on Fashion and Textiles (AIFT) Conference 2018, Hong Kong, July 3–6, 2018 (pp. 93-100). Springer International Publishing.

Wu, D. and Sun, D.-W. (2013). Colour Measurements by Computer Vision for Food Quality Control – A Review. Trends in Food Science & Technology, 29(1), 5–20.

Yasuda, Y.D.V. et al. (2022). Aircraft Visual Inspection: A Systematic Literature Review. Computers in Industry, 141, 103695.

Zhong, Z. and Ma, Z. (2021). A Novel Defect Detection Algorithm for Flexible Integrated Circuit Package Substrates. Accessed from: [25 September 2023].