A Hybrid Classification Approach for Fasteners Based on Transfer Learning with Fine-Tuning and Deep Features

Authors : Canan TAŞTİMUR, Erhan AKIN

Pages : 461-475

Doi:10.55525/tjst.1317713

View : 24 | Download : 62

Publication Date : 2023-09-01

Article Type : Research Paper

Abstract :Deep learning, which has seen frequent use in recent studies, has helped solve the problem of classifying objects of many different types and properties. Most studies both create and train a convolutional neural network insert ignore into journalissuearticles values(CNN); from scratch. The time spent training the network is thus wasted. Transfer learning insert ignore into journalissuearticles values(TL); is used both to prevent the loss of time due to training the dataset and to more effectively classify small datasets. This study performs classification using a dataset containing eighteen types of fastener. Our study contains three different TL scenarios. Two of them use TL with fine-tuning insert ignore into journalissuearticles values(FT);, while the third does so with feature extraction insert ignore into journalissuearticles values(FE);. The study compares the classification performance of eighteen different pre-trained network models insert ignore into journalissuearticles values(i.e., one or more versions of EfficientNet, DenseNet, InceptionResNetV2, InceptionV3, MobileNet, ResNet50, Xception, and VGGNet); in detail. When compared to other research in the literature, our first and second scenarios provide excellent implementations of TL-FT, while our third scenario, TL-FE, is hybrid and produces better results than the other two. Furthermore, our findings are superior to those of most previous studies. The models with the best results are DenseNet169 with an accuracy of 0.97 in the TL-FT1 scenario, EfficientNetB0 with 0.96 in TL-FT2, and DenseNet169 with 0.995 in TL-FE.
Keywords : Sınıflandırma, bağlantı elemanı, özellik çıkarma, ince ayar, Sınıflandırma, bağlantı elemanı, özellik çıkarma, ince ayar, transfer öğrenme