Mahdi HATAMİ VARJOVİ, Muhammed Fatih TALU, Kazım HANBAY

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

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,

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