Scopus
🔓 Açık Erişim YÖKSİS Eşleşti
Light weight convolutional neural network and low-dimensional images transformation approach for classification of thermal images
Case Studies in Thermal Engineering · Ocak 2023
YÖKSİS Kayıtları
Light weight convolutional neural network and low-dimensional images transformation approach for classification of thermal images
CASE STUDIES IN THERMAL ENGINEERING · 2023 SCI-Expanded
DOÇENT YAVUZ SELİM TAŞPINAR →
Makale Bilgileri
DergiCase Studies in Thermal Engineering
Yayın TarihiOcak 2023
Cilt / Sayfa41
Scopus ID2-s2.0-85145874630
Erişim🔓 Açık Erişim
Özet
Thermal energy is emitted in the infrared range between X-ray and Gamma rays, which are invisible to the human eye. Thermal cameras can detect the temperature that arises due to the heat emitted by the objects in a non-contact way and transform it into an image. These images ensure to detection of objects regardless of ambient occlusion. Based on this problem, five different classification models were proposed within the scope of the study. New low-dimensional images were obtained by extracting the features of thermal images with HOG (Histogram Oriented of Gradients), LBP (Local Binary Pattern), SIFT (Scale Invariant Feature Transform), and GF (Gabor Filter) methods. These images are classified by a CNN (Convolutional Neural Network) model called LW-CNN (Light Weight CNN). Raw thermal images were classified with the LW-CNN model without pre-processing. In order to analyze the efficiency of the proposed models, the results were compared via the pre-trained VGG16 model. Three different datasets containing thermal images were used in classification processes. The highest classification accuracy was obtained from the LW-CNN model in the performance evaluations carried out on the three datasets. With this model, the classification accuracies obtained from the datasets are 98.58%, 95.56%, and 100%, respectively.
Yazarlar (1)
1
Yavuz Selim Taspinar
ORCID: 0000-0002-7278-4241
Anahtar Kelimeler
Classification
CNN
Deep learning
Light weight
Thermal images
Kurumlar
Selçuk Üniversitesi
Selçuklu Turkey
Metrikler
7
Atıf
1
Yazar
5
Anahtar Kelime