The Effect of Virtual Reality Images on Artificial Intelligence Classification of Real Environment Images

Nur Özbek; Berna Gürler Arı; Sevgi Öztorun; Ömer Faruk Alçin

Makale detayları

The Effect of Virtual Reality Images on Artificial Intelligence Classification of Real Environment Images

Yazarlar:

Nur Özbek | Berna Gürler Arı | Sevgi Öztorun | Ömer Faruk Alçin

Yayıncı:

Fırat University

Ana Başlıklar:

Computer Software

Dergi:

Turkish Journal of Science and Technology Vol.20 Issue 1, pp.269-279

Belge Tipi:

Makale

Yayın Tarihi:

3/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001341496

pISSN:

1308-9080

Anahtar kelimeler:

Özet:

 Since virtual reality VR is a new and current field of study, it is intensively studied by researchers. Health, education, engineering, culture and tourism, architecture, military fields and many other fields of study have taken steps to support their studies with VR technology and the related subject has become the focus of many researchers. In this thesis, VR technology was used to improve the classification performance of real media images. The proposed approach consists of the classification of real environment images with transfer learning. The transfer learning mentioned in the thesis study can be defined as training an untrained deep architecture with VR images and then retraining the network with real images fine-tuning. For this purpose, VR scenes are designed in the UNITY environment. A dataset consisting of 15 environments, called V-Env15, was prepared from the designed VR scenes. The proposed approach was tested with the Scene-15 dataset, which is frequently used in environment classification studies. In the thesis, serial and parallel network and GoogLeNet and Inception-ResNet-V2 deep learning architectures were used. In the experimental studies, 0.56 higher accuracy performance increase was achieved in the serial architecture and 4.68 higher accuracy performance increase in parallel architecture. A 4.79 accuracy performance increase was achieved between GoogLeNet and the Serial network, and a 0.44 decrease was achieved between the Parallel network. A 4.47 increase was achieved between Inception-ResNet-V2 and the Serial network, and a 4.57 decrease was achieved between the Parallel network.

 Since virtual reality VR is a new and current field of study, it is intensively studied by researchers. Health, education, engineering, culture and tourism, architecture, military fields and many other fields of study have taken steps to support their studies with VR technology and the related subject has become the focus of many researchers. In this thesis, VR technology was used to improve the classification performance of real media images. The proposed approach consists of the classification of real environment images with transfer learning. The transfer learning mentioned in the thesis study can be defined as training an untrained deep architecture with VR images and then retraining the network with real images fine-tuning. For this purpose, VR scenes are designed in the UNITY environment. A dataset consisting of 15 environments, called V-Env15, was prepared from the designed VR scenes. The proposed approach was tested with the Scene-15 dataset, which is frequently used in environment classification studies. In the thesis, serial and parallel network and GoogLeNet and Inception-ResNet-V2 deep learning architectures were used. In the experimental studies, 0.56 higher accuracy performance increase was achieved in the serial architecture and 4.68 higher accuracy performance increase in parallel architecture. A 4.79 accuracy performance increase was achieved between GoogLeNet and the Serial network, and a 0.44 decrease was achieved between the Parallel network. A 4.47 increase was achieved between Inception-ResNet-V2 and the Serial network, and a 4.57 decrease was achieved between the Parallel network.

 Since virtual reality VR is a new and current field of study, it is intensively studied by researchers. Health, education, engineering, culture and tourism, architecture, military fields and many other fields of study have taken steps to support their studies with VR technology and the related subject has become the focus of many researchers. In this thesis, VR technology was used to improve the classification performance of real media images. The proposed approach consists of the classification of real environment images with transfer learning. The transfer learning mentioned in the thesis study can be defined as training an untrained deep architecture with VR images and then retraining the network with real images fine-tuning. For this purpose, VR scenes are designed in the UNITY environment. A dataset consisting of 15 environments, called V-Env15, was prepared from the designed VR scenes. The proposed approach was tested with the Scene-15 dataset, which is frequently used in environment classification studies. In the thesis, serial and parallel network and GoogLeNet and Inception-ResNet-V2 deep learning architectures were used. In the experimental studies, 0.56 higher accuracy performance increase was achieved in the serial architecture and 4.68 higher accuracy performance increase in parallel architecture. A 4.79 accuracy performance increase was achieved between GoogLeNet and the Serial network, and a 0.44 decrease was achieved between the Parallel network. A 4.47 increase was achieved between Inception-ResNet-V2 and the Serial network, and a 4.57 decrease was achieved between the Parallel network.

 Sanal gerçeklik SG yeni ve güncel bir çalışma alanı olduğundan araştırmacılar tarafından yoğun şekilde çalışılmaktadır. Sağlık, eğitim, mühendislik, kültür ve turizm, mimari, askeri alanlar ve daha birçok çalışma alanı SG teknolojisi ile çalışmalarını destekleyici adımlar atmış ve ilgili konu birçok araştırmacının odağı haline gelmiştir. Bu çalışmada gerçek ortam görüntülerinin sınıflandırma başarımını artırmak için SG teknolojisinden yararlanılmıştır. Önerilen yaklaşım transfer öğrenme ile gerçek ortam görüntülerinin sınıflandırılması işleminden oluşmaktadır. İlgili çalışmada bahsedilen transfer öğrenme, eğitilmemiş bir derin mimarinin SG görüntüleri ile eğitilmesi ardından ağın gerçek görüntülerle yeniden eğitimi fine-tuning olarak tanımlanır. UNITY ortamında tasarlanan SG sahnelerinden V-Env15 olarak isimlendirilen ve 15 ortamdan oluşan bir veri seti hazırlanmıştır. Ortam sınıflama çalışmalarında sıklıkla kullanılan Scene-15 veri seti ile önerilen yaklaşım test edilmiştir. Çalışmada tasarlanan Seri ve Paralel ağ ile GoogLeNet ve Inception-ResNet-V2 derin öğrenme mimarileri kullanılmıştır. Deneysel çalışmalarda tasarladığımız seri mimaride 0,56 ve paralel mimaride ise 4,68 daha yüksek doğruluk performans artışı elde edilmiştir. GoogLeNet ile Seri ağ arasında performasn doğruluğu açısından 4,79 artış, Paralel ağ arasında 0,44 azalma elde edilmiştir. Inception-ResNet-V2 ile Seri ağ arasında 4,47 artış, Paralel ağ arasında 4,57 azalma elde edilmiştir.

Makale detayları

The Effect of Virtual Reality Images on Artificial Intelligence Classification of Real Environment Images

Yazarlar:

Nur Özbek | Berna Gürler Arı | Sevgi Öztorun | Ömer Faruk Alçin

Yayıncı:

Fırat University

Ana Başlıklar:

Computer Software

Dergi:

Turkish Journal of Science and Technology Vol.20 Issue 1, pp.269-279

Belge Tipi:

Makale

Yayın Tarihi:

3/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001341496

pISSN:

1308-9080

Virtual Reality

Machine Learning

Image Processing

Environment Classification.Sanal Gerçeklik

Makine Öğrenmesi

Görüntü İşleme

Ortam Sınıflandırma

Sayı

Makale detayları

Yazarlar: Nur Özbek | Berna Gürler Arı | Sevgi Öztorun | Ömer Faruk Alçin

Yayıncı: Fırat University

Ana Başlıklar: Computer Software

Dergi: Turkish Journal of Science and Technology Vol.20 Issue 1, pp.269-279

Belge Tipi: Makale

Yayın Tarihi: 3/2025

Belge Dili: İngilizce

Document ID (Org No.): 2001341496

pISSN: 1308-9080

Sayı

Yazarlar:

Nur Özbek | Berna Gürler Arı | Sevgi Öztorun | Ömer Faruk Alçin

Yayıncı:

Fırat University

Ana Başlıklar:

Computer Software

Dergi:

Turkish Journal of Science and Technology Vol.20 Issue 1, pp.269-279

Belge Tipi:

Makale

Yayın Tarihi:

3/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001341496

pISSN:

1308-9080