Derin Öznitelik ve İnce-Ayar ile Aktarım Öğrenme Tabanlı Bağlantı Elemanlarının Hibrit Sınıflandırma Yaklaşımı

Year 2023, Volume: 18 Issue: 2, 461 - 475, 01.09.2023

Canan Taştimur , Erhan Akın

https://doi.org/10.55525/tjst.1317713

Abstract

Son yıllarda yapılan çalışmalarda sıkça kullanılmaya başlanan derin öğrenme, birçok farklı tür ve özellikteki nesnelerin sınıflandırılması sorununun çözülmesine yardımcı olmuştur. Çoğu çalışma, sıfırdan bir evrişimsel sinir ağı (CNN) oluşturur ve eğitir. Ağı eğitmek için harcanan zaman böylece boşa harcanır. Transfer öğrenme (TL) hem veri setinin eğitilmesinden kaynaklanan zaman kaybını önlemek hem de küçük veri setlerini daha etkin bir şekilde sınıflandırmak için kullanılmaktadır. Bu çalışma, on sekiz tip bağlantı elemanı içeren bir veri seti kullanarak sınıflandırma yapmaktadır. Çalışmamız üç farklı TL senaryosu içermektedir. Bunlardan ikisi ince ayar (FT) ile TL kullanırken, üçüncüsü özellik çıkarma (FE) ile yapmaktadır. Çalışma, on sekiz farklı önceden eğitilmiş ağ modelinin (yani EfficientNet, DenseNet, InceptionResNetV2, InceptionV3, MobileNet, ResNet50, Xception ve VGGNet) sınıflandırma performansını ayrıntılı olarak karşılaştırmaktadır. Literatürdeki diğer araştırmalarla karşılaştırıldığında, birinci ve ikinci senaryolarımız TL-FT' nin iyi sonuçlarla uygulamalarını sağlarken, üçüncü senaryomuz TL-FE hibrit bir yöntem olup diğer iki senaryodan daha iyi sonuçlar üretmiştir. Ayrıca, bulgularımız literatürdeki çalışmaların çoğundan daha üstün olduğu fark edilmiştir. En iyi sonuçlara sahip modeller TL-FT1 senaryosunda 0,97 doğrulukla DenseNet169, TL-FT2'de 0,96 ile EfficientNetB0 ve TL-FE'de 0,995 ile DenseNet169'dur.

Keywords

Sınıflandırma, bağlantı elemanı, özellik çıkarma, ince ayar, transfer öğrenme

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

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 (CNN) from scratch. The time spent training the network is thus wasted. Transfer learning (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 (FT), while the third does so with feature extraction (FE). The study compares the classification performance of eighteen different pre-trained network models (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

Classification, fastener, feature extraction, fine tuning, transfer learning

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