BROKEN STITCH DETECTION METHOD FOR SEWING OPERATION USING CNN FEATURE MAP AND IMAGE-PROCESSING TECHNIQUES

Yıl 2023, Cilt: 7 Sayı: 2, 217 - 233, 31.12.2023

Timur İnan , Samah Noaman Sahi Sahı

https://doi.org/10.53600/ajesa.1374410

Öz

Quality in industrial processes has become increasingly important and cost reduction and process optimization are becoming increasingly necessary Quality control brings increased production and even increased profits for a process. It can be said, then, that it is the most important metric when it comes to production. It is extremely difficult to have a 100% defect-free manufacturing process. One of the industrial processes that has received such attention regarding defects is the weaving process. The present work will make a global study on Machine Learning techniques and also on Wavelets. This study may serve as a basis for future academic work. The application built in the present work will also serve as an example of how a computer vision system can vary from the classifier algorithm used to the feature extraction technique, which in this case, will use the Wavelet Transform. In this work we Survey the state of the art in methods of recognizing defects in fabrics. We will also Create the database, as well as the set of images to be used. Extract information from the image with the Wavelet Transform. Test different classification algorithms in order to find the best answer for this problem. Improve the performance of the classifier algorithms through the CNN algorithm. Validate the system using the k-fold cross validation technique.

Anahtar Kelimeler

CNN, DL, CL, WT

Etik Beyan

Hello dear editor I hope you are doing well , I have submitted my article to your journal and I hope that I could publish in it soon to continue my masters degree in computer engineering .so I hope you to check my submission and inform me if there is something missing .with best wishes. SAMAH SAHI STUDENT IN ALTINBAS UNIVERSITY COLLEGUE OF COMPUTER ENGINEERING.

Destekleyen Kurum

altinbas university

Kaynakça

• Anandan, P., & Sabeenian, R. S. (2018). Fabric defect detection using discrete curvelet transform. Procedia computer science, 133, 1056-1065.
• Ben Gharsallah, M., & Ben Braiek, E. (2021). A visual attention system based anisotropic diffusion method for an effective textile defect detection. The Journal of The Textile Institute, 112(12), 1925-1939.
• Bullon, J., González Arrieta, M. A., Hernández Encinas, A., & Queiruga Dios, M. A. (2017). Manufacturing processes in the textile industry. Expert Systems for fabrics production.
• Chetverikov, D., & Hanbury, A. (2002). Finding defects in texture using regularity and local orientation. Pattern Recognition, 35(10), 2165-2180.
• Habib, M. T., Shuvo, S. B., Uddin, M. S., & Ahmed, F. (2016, December). Automated textile defect classification by Bayesian classifier based on statistical features. In 2016 International Workshop on Computational Intelligence (IWCI) (pp. 101-105). IEEE.
• Hu, J. (Ed.). (2011). Computer technology for textiles and apparel. Elsevier. Kumar, P. S., & Hafedh, H. (2013). Detection of defects in knitted fabric images using Eigen values. International Journal Computer Science Engineering-IJASCSE, 2(3), 7-10.
• Li, C., Li, J., Li, Y., He, L., Fu, X., & Chen, J. (2021). Fabric defect detection in textile manufacturing: a survey of the state of the art. Security and Communication Networks, 2021, 1-13.
• Liu, Y., Lee, J. M., & Lee, C. (2020). The challenges and opportunities of a global health crisis: the management and business implications of COVID-19 from an Asian perspective. Asian Business & Management, 19, 277-297
• Ngan, H. Y., Pang, G. K., & Yung, N. H. (2011). Automated fabric defect detection—A review. Image and vision computing, 29(7), 442-458.
• Raheja, J. L., Ajay, B., & Chaudhary, A. (2013). Real time fabric defect detection system on an embedded DSP platform. Optik, 124(21), 5280-5284.
• Raheja, J. L., Kumar, S., & Chaudhary, A. (2013). Fabric defect detection based on GLCM and Gabor filter: A comparison. Optik, 124(23), 6469-6474.
• Sayed, M. S. (2016, October). Robust fabric defect detection algorithm using entropy filtering and minimum error thresholding. In 2016 IEEE 59th International Midwest Symposium on Circuits and Systems (MWSCAS) (pp. 1-4). IEEE.
• Sibly Sadik, M., & Islam, S. (2014). Report on defects of woven fabrics and their remedies (Doctoral dissertation, Daffodil International University).
• Thoben, K. D., Wiesner, S., & Wuest, T. (2017). “Industrie 4.0” and smart manufacturing-a review of research issues and application examples. International journal of automation technology, 11(1), 4-16.
• Xu, X., Cao, D., Zhou, Y., & Gao, J. (2020). Application of neural network algorithm in fault diagnosis of mechanical intelligence. Mechanical Systems and Signal Processing, 141, 106625.
• Yan, K., Liu, L., Xiang, Y., & Jin, Q. (2020). Guest editorial: AI and machine learning solution cyber intelligence technologies: new methodologies and applications. IEEE Transactions on Industrial Informatics, 16(10), 6626-6631.