Fabric Defect Detection Using Customized Deep Convolutional Neural Network for Circular Knitting Fabrics

Year 2022, , 160 - 165, 29.09.2022

Mahdi Hatami Varjovi , Muhammed Fatih Talu , Kazım Hanbay

https://doi.org/10.46810/tdfd.1108264

Cited By: 1

Abstract

Visual inspection is a main stage of quality assurance process in many applications. In this paper, we propose a new network architecture for detecting the fabric defects based on convolutional neural network. Four different pre-trained and customized model network architectures have compared in terms of performance. Results has been evaluated on a fabric defect dataset of 13.800 images. Among the existing Inception V3, MobileNetV2, Xception and ResNet50 methods, the InceptionV3 model has achieved 78% classification success. Our designed deep network model could achieve 97% success. The experimental works show that the designed deep model is effective in detecting the fabric defects.

Keywords

Fabric defect classification, Deep learning, Neural networks, Model optimization

Supporting Institution

The Turkish Scientific and Technological Research Council. (TÜBİTAK)

Project Number

5180054

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