Varjovi, Mahdi HatamiTalu, Muhammed FatihHanbay, Kazım2024-08-042024-08-0420222149-6366https://doi.org/10.46810/tdfd.1108264https://search.trdizin.gov.tr/yayin/detay/1127670https://hdl.handle.net/11616/90036Visual 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.eninfo:eu-repo/semantics/openAccessArticle11316016510.46810/tdfd.11082641127670