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Identification of Defects in the Fabric using Deep Convolutional Neural Networks

Janeja, Lavneet (2020) Identification of Defects in the Fabric using Deep Convolutional Neural Networks. Masters thesis, Dublin, National College of Ireland.

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The inspection of defects in the fabrics is one of the essential steps before manufacturing them in finished goods. General defects like spills or stains are often easily by the human eyes but when it comes to inspect the defects in details then it becomes difficult for the humans to detect accurately at a swift pace. This is why more efforts are drawn towards building up models, especially using Tesnorflow and Keras which are self-capable of minutely inspecting the defects with certain accuracy at a more efficient pace than humans. This project aims in developing a new pre-trained model called Dual Channel Convolutional Neural Network that uses two channels (one deep and other shallow) for classifying the defects in fabrics and comparing its performance with other pre-trained models. The research project was implemented using DAGM dataset downloaded from Upon comparing the evaluation results of validation and training datasets, it was concluded that even though there was no significant difference between all the five models (VGG16, AlexNet, VGG19, MobileNet and DCCNN) but still the developed model out-performed three of the four models (VGG16, AlexNet and VGG19) in terms of precision, recall and F1 score values.

Item Type: Thesis (Masters)
Subjects: Q Science > QA Mathematics > Electronic computers. Computer science
T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science
Q Science > QA Mathematics > Computer software
T Technology > T Technology (General) > Information Technology > Computer software
Divisions: School of Computing > Master of Science in Data Analytics
Depositing User: Dan English
Date Deposited: 22 Jan 2021 14:22
Last Modified: 22 Jan 2021 14:22

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