Enhancing textile quality control with the application of teachable machine and Raspberry Pi as machine learning-based image processing

Emmanuel Agung Nugroho; Joga Dharma Setiawan; M. Munadi; M. Diki

doi:10.30811/jpl.v22i5.5308

Enhancing textile quality control with the application of teachable machine and Raspberry Pi as machine learning-based image processing

Emmanuel Agung Nugroho, Joga Dharma Setiawan, M. Munadi, M. Diki

Abstract

The adoption of image processing-based technologies in the textile sector is rising. This technology is commonly utilized to replace traditional sensor systems that are limited to a single function while also improving product quality control functions. Defects during the manufacturing process are a common problem in the textile business, particularly with fabric products. This study created a fabric quality control system that detects fabric problems using machine learning-based picture classification techniques. A D320p web camera detects rare and slap flaws, which are classified using open-source Google teaching machine software and processed on a Raspberry Pi 3B device. The laboratory-scale measurement was carried out on a prototype cloth rolling machine using the confusion matrix method. The test results reveal an average inference speed of 143.5 milliseconds, a frame rate of 6.45 fps, and a 98.56% accuracy rate. These results demonstrate that the proposed system is effective and efficient for detecting fabric defects, offering a promising solution for enhancing quality control in the textile industry. Future research could focus on scaling the system for industrial use and enhancing real-time performance.

Keywords

machine learning; image classification; Google teachable machine; Raspberry Pi-4; confusion matrix

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References

S. Kasus, P. T. Iskandar, I. Printing, D. F. Dewanti, and D. Pujotomo, “Analisis Penyebab Cacat Produk Kain Dengan Menggunakan Metode Failure Mode And Effect Analysis ( Fmea )”.

S. I. Kampezidou, A. T. Ray, A. P. Bhat, O. J. P. Fischer, and D. N. Mavris, “Fundamental Components and Principles of Supervised Machine Learning Workflows with Numerical and Categorical Data,” pp. 384–416, 2024.

I. D. Id, “Machine Learning : Teori , Studi Kasus dan Implementasi Menggunakan Python,” no. July, 2021, doi: 10.5281/zenodo.5113507.

M. Nurhadi and J. Purnomo, “Implementation of Image Classification Using Convolutional Neural Network (Cnn) Algorithm on Vehicles Images,” ASEAN J. Syst. Eng., vol. 6, no. 1, pp. 1–5, 2022, doi: 10.22146/ajse.v6i1.72411.

K. Azmi, S. Defit, and S. Sumijan, “Implementasi Convolutional Neural Network (CNN) Untuk Klasifikasi Batik Tanah Liat Sumatera Barat,” J. Unitek, vol. 16, no. 1, pp. 28–40, 2023, doi: 10.52072/unitek.v16i1.504.

C. R. Kotta, D. Paseru, and M. Sumampouw, “Implementasi Metode Convolutional Neural Network untuk Mendeteksi Penyakit Pada Citra Daun Tomat,” J. Pekommas, vol. 7, no. 2, pp. 123–132, 2022, doi: 10.56873/jpkm.v7i2.4961.

Buyut Khoirul Umri and V. Delica, “Penerapan transfer learning pada convolutional neural networks dalam deteksi covid-19.,” Jnanaloka, pp. 9–17, 2021, doi: 10.36802/jnanaloka.2021.v2-no2-9-17.

J. Sanjaya and M. Ayub, “Augmentasi Data Pengenalan Citra Mobil Menggunakan Pendekatan Random Crop, Rotate, dan Mixup,” J. Tek. Inform. dan Sist. Inf., vol. 6, no. 2, pp. 311–323, 2020, doi: 10.28932/jutisi.v6i2.2688.

Y. Religia, “Feature Extraction Untuk Klasifikasi Pengenalan Wajah Menggunakan Support Vector Machine Dan K-Nearest Neighbor,” Pelita Teknol. J. Ilm. Inform. Arsit. dan Lingkung., vol. 14, no. 2, pp. 85–92, 2019.

N. Handa, Y. Kaushik, N. Sharma, M. Dixit, and M. Garg, “Image Classification Using Convolutional Neural Networks,” Commun. Comput. Inf. Sci., vol. 1393, no. December 2022, pp. 510–517, 2021, doi: 10.1007/978-981-16-3660-8_48.

D. Jaswal, S. V, and K. P. Soman, “Image Classification Using Convolutional Neural Networks,” Int. J. Sci. Eng. Res., vol. 5, no. 6, pp. 1661–1668, 2014, doi: 10.14299/ijser.2014.06.002.

K. Diantoro, B. Adriasyah, C. Integral, and P. C. Awal, “Sistem Identifikasi Jenis Burung Dengan Image,” vol. 20, no. 1, pp. 96–105, 2019.

Y. Boussemart, M. L. Cummings, J. Las Fargeas, and N. Roy, “Supervised vs unsupervised learning for operator state modeling in unmanned vehicle settings,” J. Aerosp. Comput. Inf. Commun., vol. 8, no. 3, pp. 71–85, 2011, doi: 10.2514/1.46767.

P. Cheng, B. Chien, and W. Yang, “Medical Image Classification by Supervised Machine Learning,” no. June, 2014.

A. Lindholm, N. Wahlström, F. Lindsten, and T. B. Schön, “Supervised Machine Learning: Statistical Machine Learning course,” p. 112, 2019, [Online]. Available: http://www.it.uu.se/edu/course/homepage/sml/literature/lecture_notes.pdf

A. N. Angelopoulos et al., “Image-to-Image Regression with Distribution-Free Uncertainty Quantification and Applications in Imaging,” Proc. Mach. Learn. Res., vol. 162, pp. 717–730, 2022.

D. P. Mishra, S. Mishra, S. Jena, and S. R. Salkuti, “Image classification using machine learning,” Indones. J. Electr. Eng. Comput. Sci., vol. 31, no. 3, pp. 1551–1558, 2023, doi: 10.11591/ijeecs.v31.i3.pp1551-1558.

A. Olaode, G. Naghdy, and C. Todd, “Unsupervised classification of images: a review,” Int. J. Image Process., no. 8.5, pp. 325–342, 2014, [Online]. Available: https://www.researchgate.net/profile/Abass-Olaode/publication/265729668_Unsupervised_Classification_of_Images_A_Review/links/541a74be0cf203f155ae295a/Unsupervised-Classification-of-Images-A-Review.pdf

J. Goldberger, S. Gordon, and H. Greenspan, “Unsupervised image-set clustering using an information theoretic framework,” IEEE Trans. Image Process., vol. 15, no. 2, pp. 449–458, 2006, doi: 10.1109/TIP.2005.860593.

M. Dash, H. Liu, and J. Yao, “Dimensionality reduction of unsupervised data,” Proc. Int. Conf. Tools with Artif. Intell., no. April, pp. 532–539, 1997, doi: 10.1109/tai.1997.632300.

S. Afaq and S. Rao, “Significance Of Epochs On Training A Neural Network,” Int. J. Sci. Technol. Res., vol. 9, no. 06, pp. 1–4, 2020, [Online]. Available: www.ijstr.org

A. Ali, R. Pinciroli, F. Yan, and E. Smirni, “Batch: Machine learning inference serving on serverless platforms with adaptive batching,” Int. Conf. High Perform. Comput. Networking, Storage Anal. SC, vol. 2020-Novem, 2020, doi: 10.1109/SC41405.2020.00073.

P. M. Radiuk, “Impact of Training Set Batch Size on the Performance of Convolutional Neural Networks for Diverse Datasets,” Inf. Technol. Manag. Sci., vol. 20, no. 1, pp. 20–24, 2018, doi: 10.1515/itms-2017-0003.

N. Rochmawati, H. B. Hidayati, Y. Yamasari, H. P. A. Tjahyaningtijas, W. Yustanti, and A. Prihanto, “Analisa Learning Rate dan Batch Size pada Klasifikasi Covid Menggunakan Deep Learning dengan Optimizer Adam,” J. Inf. Eng. Educ. Technol., vol. 5, no. 2, pp. 44–48, 2021, doi: 10.26740/jieet.v5n2.p44-48.

DOI: http://dx.doi.org/10.30811/jpl.v22i5.5308