Light weight convolutional neural network and low-dimensional images transformation approach for classification of thermal images
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 o...
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| Format: | Article |
| Language: | English |
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Elsevier
2023-01-01
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| Series: | Case Studies in Thermal Engineering |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2214157X22009078 |
| _version_ | 1797956173936197632 |
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| author | Yavuz Selim Taspinar |
| author_facet | Yavuz Selim Taspinar |
| author_sort | Yavuz Selim Taspinar |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-10T23:45:54Z |
| format | Article |
| id | doaj.art-7294d92b4e6f4e928aad75cb0f9ada7c |
| institution | Directory Open Access Journal |
| issn | 2214-157X |
| language | English |
| last_indexed | 2024-04-10T23:45:54Z |
| publishDate | 2023-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Case Studies in Thermal Engineering |
| spelling | doaj.art-7294d92b4e6f4e928aad75cb0f9ada7c2023-01-11T04:29:24ZengElsevierCase Studies in Thermal Engineering2214-157X2023-01-0141102670Light weight convolutional neural network and low-dimensional images transformation approach for classification of thermal imagesYavuz Selim Taspinar0Doganhisar Vocational School, Selcuk University, Konya, TurkeyThermal 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.http://www.sciencedirect.com/science/article/pii/S2214157X22009078Thermal imagesLight weightCNNClassificationDeep learning |
| spellingShingle | Yavuz Selim Taspinar Light weight convolutional neural network and low-dimensional images transformation approach for classification of thermal images Case Studies in Thermal Engineering Thermal images Light weight CNN Classification Deep learning |
| title | Light weight convolutional neural network and low-dimensional images transformation approach for classification of thermal images |
| title_full | Light weight convolutional neural network and low-dimensional images transformation approach for classification of thermal images |
| title_fullStr | Light weight convolutional neural network and low-dimensional images transformation approach for classification of thermal images |
| title_full_unstemmed | Light weight convolutional neural network and low-dimensional images transformation approach for classification of thermal images |
| title_short | Light weight convolutional neural network and low-dimensional images transformation approach for classification of thermal images |
| title_sort | light weight convolutional neural network and low dimensional images transformation approach for classification of thermal images |
| topic | Thermal images Light weight CNN Classification Deep learning |
| url | http://www.sciencedirect.com/science/article/pii/S2214157X22009078 |
| work_keys_str_mv | AT yavuzselimtaspinar lightweightconvolutionalneuralnetworkandlowdimensionalimagestransformationapproachforclassificationofthermalimages |