- Türk Doğa ve Fen Dergisi
- Vol: 11 Issue: 3
- Fabric Defect Detection Using Customized Deep Convolutional Neural Network for Circular Knitting Fab...
Fabric Defect Detection Using Customized Deep Convolutional Neural Network for Circular Knitting Fabrics
Authors : Mahdi Hatami Varjovi, Muhammed Fatih Talu, Kazım Hanbay
Pages : 160-165
Doi:10.46810/tdfd.1108264
View : 19 | Download : 3
Publication Date : 2022-09-29
Article Type : Research
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