A Hybrid Machine Learning Approach to Fabric Defect Detection and Classification

Mohammed, Swash Sami; Clarke, Hülya Gökalp

doi:10.1007/978-3-031-01984-5_11

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2023

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“…Following that, the VGG16 and Random Forest approaches were employed to classify the defects that were present in the fabric. The findings of the investigation showed that they were able to identify errors with a precision of 99.3 percent [12].…”

Section: Related Workmentioning

confidence: 94%

Ensemble learning application for textile defect detection

Guder,

Isik,

Anagun

2023

ijamec

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Textile production has an important share in the Turkish economy. One of the common problems in textile factories in Turkey is fabric texture defects that may occur due to textile machinery. The faulty production of the fabric adversely affects the company's economy and prestige. Many methods have been developed to achieve high accuracy in detecting defects in fabric. The aim of this study is to compare the performance of the models using the new dataset and deep learning models. The findings have determined that the Seresnet152d model, which is one of the transfer learning models, can classify with 95.38% accuracy on the generated dataset. Moreover, the majority voting gives 95.58% accuracy rate. In order to achieve high accuracy in the future, it is planned to optimize the parameters of the models used in the study with the help of swarm-oriented optimization algorithms.

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Section: Related Workmentioning

confidence: 94%