GMRNet: A Novel Geometric Mean Relation Network for Few-Shot Very Similar Object Classification

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TAŞTİMUR C., Akin E.

IEEE Access, cilt.10, ss.97360-97369, 2022 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 10
  • Basım Tarihi: 2022
  • Doi Numarası: 10.1109/access.2022.3206528
  • Dergi Adı: IEEE Access
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Compendex, INSPEC, Directory of Open Access Journals
  • Sayfa Sayıları: ss.97360-97369
  • Anahtar Kelimeler: Measurement, Deep learning, Fasteners, Feature extraction, Visualization, Classification, deep metric learning, few shot learning, relation network
  • Erzincan Binali Yıldırım Üniversitesi Adresli: Evet

Özet

© 2013 IEEE.With the widespread use of Deep Learning (DL), the use of DL has increased to provide a solution to the problem of object recognition and classification. In addition to classifying many different types of objects, the Deep Metrics Learning(DML) technique is effective in classifying objects that are visually very similar to each other. In this study, a novel Relation Network (RN) based DML has been designed to classify objects in two different datasets we created. We distinguished groups of objects that had a high degree of similarity to each other. These objects have been categorized using few-shot learning(FSL) since they are quite similar to one another. The impact of changing the number of classes and samples in the database on the network's performance has been studied. It is shown how the network's accuracy varies depending on the N-way (number of classes) and K-shots (number of samples) combinations used in its design. Additionally, the performance of the network has improved by an average of 15% thanks to the contribution of the recently introduced geometric mean module to the RN in our study. The accuracy rate of our recommended RN in screw and spare parts datasets is 96.1% and 92.3%, respectively. The first dataset consists of 1800 screw images with 18 classes, while the second dataset consists of 4100 spare parts images with 20 classes. The effectiveness of our method is expressed by the two datasets that we have extensively experimentally studied.