INTERNATIONAL JOURNAL OF SCIENTIFIC DEVELOPMENT AND RESEARCH International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2455-2631 | Impact factor: 8.15 | ESTD Year: 2016
open access , Peer-reviewed, and Refereed Journals, Impact factor 8.15
Quality examination is an important aspect of ultramodern textile manufacturing. In cloth testing, automate fabric examination is important for maintain the fabric quality. The current fabric blights examination process relies on manual visual inspection, which is insufficient and costly. Therefore, there is a need for automated fabric disfigurement examination to decrease the expenses and time wasted due to blights. The development of completely automated web examination system requires robust and effective fabric disfigurement discovery algorithms. The discovery of original fabric blights is one of the most interesting problems in computer vision. Texture analysis plays an important part in the automated visual examination of texture images to discover their defects. colourful approaches for fabric disfigurement discovery have been proposed in history and the purpose of this paper is to classify and describe these algorithms. This paper attempts to present the check on fabric disfigurement d
Keywords:
Neural Network, Object Detection, Raspberry pi, Data annotation, Deep Learning, YOLO V8, Prediction, Defect Detection.
Cite Article:
"Fabric Defect Detection Using Deep Learning", International Journal of Science & Engineering Development Research (www.ijsdr.org), ISSN:2455-2631, Vol.8, Issue 5, page no.1960 - 1963, May-2023, Available :http://www.ijsdr.org/papers/IJSDR2305310.pdf
Downloads:
000337216
Publication Details:
Published Paper ID: IJSDR2305310
Registration ID:206754
Published In: Volume 8 Issue 5, May-2023
DOI (Digital Object Identifier):
Page No: 1960 - 1963
Publisher: IJSDR | www.ijsdr.org
ISSN Number: 2455-2631
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