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  Published Paper Details:

  Paper Title

FABRIC FAULT DETECTION USING DEEP TRANSFER LEARNING

  Authors

  Shreyas Zadrao,  Vipul Shewale,  Atharva Waze,  Madhuri Ghuge

  Keywords

AUC,Deep transfer learning,classification,fabric,precision

  Abstract


Defect Detection in modern material assembling is a fundamental requirement for which productive practical arrangements must be sought. In ongoing years, a few distinct techniques have been created to recognize these deformities, some of which have been fruitful to a more prominent or lesser degree. Traditionally, the acknowledgment of texture has a lot of difficulties because of its manual visual examination. Additionally, the methodologies dependent on early AI calculations legitimately rely upon high-quality highlights, which are tedious and blunder inclined cycles. Hence, an automated system is needed for the classification of fabric to improve productivity. In this paper, we propose a solution in which a deep transfer learning model will be trained on a fabric dataset. The training process will include various data augmentation techniques like rescaling, zooming, horizontal flipping, etc. The model extracts important features from a given image and classifies automatically in an end-to-end fashion. We assessed the consequences of our model utilizing assessment measurements, for example, exactness, adjusted precision,AUC and Recall.

  IJCRT's Publication Details

  Unique Identification Number - IJCRT2102646

  Paper ID - 203964

  Page Number(s) - 5330-5333

  Pubished in - Volume 9 | Issue 2 | February 2021

  DOI (Digital Object Identifier) -   

  Publisher Name - IJCRT | www.ijcrt.org | ISSN : 2320-2882

  E-ISSN Number - 2320-2882

  Cite this article

  Shreyas Zadrao,  Vipul Shewale,  Atharva Waze,  Madhuri Ghuge,   "FABRIC FAULT DETECTION USING DEEP TRANSFER LEARNING", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.9, Issue 2, pp.5330-5333, February 2021, Available at :http://www.ijcrt.org/papers/IJCRT2102646.pdf

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ISSN: 2320-2882
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ISSN and 7.97 Impact Factor Details


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ISSN: 2320-2882
Impact Factor: 7.97 and ISSN APPROVED
Journal Starting Year (ESTD) : 2013
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