Design of image classification system for fabric inspection process using Raspberry Pi

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

Abstract

This research is designed as a prototype of defect inspection system on fabric production using machine learning-based image processing technology using the open source Google teachable machine application integrated with Raspberry Pi-3B. The prototype of fabric defect inspection system is built by utilizing two rollers that function as a fabric roll house before and after the inspection process. On both rollers, a fabric is stretched to be inspected, so that from a roll of fabric with a certain length, it can be seen how many defects occur on the fabric. The inspection system is carried out using a web camera with a certain level of lighting connected to a raspberry pi as a control device. Raspberry Pi functions as an image processing device and fabric rolling motor controller. In addition to the category of fabric in good condition, this system classifies into two categories of defects, namely slap defects and sparse defects. The test results show that this system has an average frame per second (FPS) of 4.85, an average inference time of 181.1 ms, with an accuracy of image classification results of 98.4 %.

10.55981/j.mev.2024.863


Keywords


machine learning; image processing; Raspberry Pi; inference time; image classification

Full Text:

PDF


References


S. Tereza, “Balancing global success with local production challenges in the garment manufacturing sector, Textile Industry Triumph:,” Amberd Buletin, pp. 38–47, 2023.

I. A. Juliannisa, P. Widodo, T. W. Nunuk, “Success Deteriminan of the Manufacturing Industry Sector to Survive During the Pandemic in Indonesia,” J. Econ. Financ. Manag. Stud., vol. 05, no. 08, pp. 2455–2467, 2022.

L. Petrie, “Sustainability and Circularity in the Textile Value Chain - A Global Roadmap Textile Value Chain,” U. N. Environment Programme, 2023.

P. N. Aurelia, A. Firdiansjah, and H. Respati, “The Application of Service Quality and Brand Image to Maintain Customer Loyalty by Utilizing Satisfaction Strategy in Insurance Companies,” Int. J. Adv. Sci. Res. Eng., vol. 5, no. 4, pp. 223–239, 2019.

İ. İlhan, “Concept of Industry 4.0 in Textile Manufacturing Processes,” Pamukkale Univ. J. Eng. Sci., vol. 25, no. 7, pp. 810–823, 2019.

J. Butt, “A strategic roadmap for the manufacturing industry to implement industry 4.0,” Designs, vol. 4, no. 2, pp. 1–31, 2020.

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.

S. Sha et al., “Image Classification and Restoration of Ancient Textiles Based on Convolutional Neural Network,” Int. J. Comput. Intell. Syst., vol. 17, no. 1, 2024.

M. Seçkin, A. Ç. Seçkin, P. Demircioglu, and I. Bogrekci, “FabricNET: A Microscopic Image Dataset of Woven Fabrics for Predicting Texture and Weaving Parameters through Machine Learning,” Sustainability, vol. 15, no. 21, p. 15197, 2023.

B. G. Batchelor, “Machine Vision for Industrial Applications BT - Machine Vision Handbook,” B. G. Batchelor, Ed., London: Springer London, 2012, pp. 1–59.

J. Xu, Y. Wei, A. Wang, H. Zhao, and D. Lefloch, “Analysis of Clothing Image Classification Models: A Comparison Study between Traditional Machine Learning and Deep Learning Models,” Fibres Text. East. Eur., vol. 30, no. 5, pp. 66–78, 2022.

K. Wisnudhanti and F. Candra, “Image Classification of Pandawa Figures Using Convolutional Neural Network on Raspberry Pi 4,” J. Phys. Conf. Ser., vol. 1655, no. 1, 2020.

E. Pintelas, M. Liaskos, I. E. Livieris, S. Kotsiantis, and P. Pintelas, “Explainable machine learning framework for image classification problems: Case study on glioma cancer prediction,” J. Imaging, vol. 6, no. 6, 2020.

H. Alaskar and T. Saba, “Machine Learning and Deep Learning: A Comparative Review,” Prince Sultan University, Researchgate, June, pp. 143–150, 2021.

V. Radojcic and P. Spalevic, “Advancements in Computer Vision Applications for Traffic Surveillance Systems,” Sinergija University Scientific Conference, February, pp. 10–15, 2023.

R. Sathya, A. Asha, K. Muralikrishna, and V. Bakyalakshmi, “Bespoke Measurement based on Convolutional Neural Network using OpenCV Bespoke Measurement based on Convolutional Neural Network using OpenCV,” Ijatcse, vol. 9, no. 3, May, 2020.

Ibnu. Daqiqil. Id, (2021, Juli) “Machine Learning : Teori , Studi Kasus dan Implementasi Menggunakan Python,” Riau, UR PRESS.

Bonaccorso. G.,(2017, Juli), "Machine Learning Algorithms." Birmingham: Packt Publishing.Ltd.

Ş. Çiğdem, I. Meidute-Kavaliauskiene, and B. Yıldız, “Industry 4.0 and Industrial Robots: A Study from the Perspective of Manufacturing Company Employees,” Logistics, vol. 7, no. 1, 2023.

V. Wiley and T. Lucas, “Computer Vision and Image Processing: A Paper Review,” Int. J. Artif. Intell. Res., vol. 2, no. 1, p. 22, 2018.

R. V. Sharan and G. C. Onwubolu, “Simulating the Arm Movements of a Stepper Motor Controlled Pick-and-Place Robot Using the Stepper Motor Model,” Int. J. Adv. Sci. Technol., vol. 60, pp. 59–66, 2013.

E. Ariesto, U. Malahina, R. P. Hadjon, and F. Y. Bisilisin, “Teachable Machine : Real-Time Attendance of Students Based on Open Source System,” Ijics, vol. 6, no. 3, pp. 140–146, 2022.

E. A. Nugroho, J. D. Setiawan, and M. Munadi, “Handling Four DOF Robot to Move Objects Based on Color and Weight using Fuzzy Logic Control,” jrc, vol. 4, no. 6, pp. 769–779, 2023.

P. S. Tsai, T. F. Wu, J. Y. Chen, and F. H. Lee, “Drawing system with dobot magician manipulator based on image processing,” Machines, vol. 9, no. 12, Dec. 2021.

H. Al-Jubouri, H. Du, and H. Sellahewa, “Adaptive clustering based segmentation for image classification,” 2013 5th Comput. Sci. Electron. Eng. Conf. CEEC 2013 - Conf. Proc., April, pp. 128–133, 2013.

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.

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.

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.

E. A. U. Malahina, M. Saitakela, S. J. Bulan, M. I. J. Lamabelawa, and Y. S. Belutowe, “Teachable Machine: Optimization of Herbal Plant Image Classification Based on Epoch Value, Batch Size and Learning Rate,” J. Appl. Data Sci., vol. 5, no. 2, pp. 532–545, 2024.


Article Metrics

Metrics Loading ...

Metrics powered by PLOS ALM

Refbacks

  • There are currently no refbacks.


Bookmark and Share


Copyright (c) 2024 Journal of Mechatronics, Electrical Power, and Vehicular Technology

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.