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

Year 2023, , 217 - 233, 31.12.2023

Timur İnan , Samah Noaman Sahi Sahı

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

Abstract

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.

Keywords

CNN, DL, CL, WT

Ethical Statement

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.

Supporting Institution

altinbas university

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