Betül BEKTAŞ EKİCİ, Saltuk Taha USTAOĞLU

Binalarda Fiziksel Hasar Tespiti için Derin Öğrenme: Transfer Öğrenme Yöntemlerinin Karşılaştırılması

Binalardaki fiziksel hasarın tespiti, yapıların güvenliğini ve bütünlüğünü sağlamada kritik bir görevdir. Bu çalışmada, özellikle çatlaklar, kusurlar, nem ve hasarsız sınıflara odaklanarak binalardaki fiziksel hasarı tespit etmek için derin öğrenme yöntemlerinin etkinliği araştırılmıştır. VGG16, GoogLeNet ve ResNet50 dahil olmak üzere transfer öğrenme yöntemleri, 7200 görüntüden oluşan bir veri kümesini sınıflandırmak için kullanılmıştır. Veri kümesi eğitim, doğrulama ve test kümelerine ayrılmış ve modellerin performansı doğruluk, kesinlik, geri çağırma ve F1-skoru gibi ölçütler kullanılarak değerlendirilmiştir. Sonuçlar, üç modelin de test setinde yüksek doğruluk elde ettiğini, VGG16 ve ResNet50'nin GoogLeNet'ten daha iyi performans gösterdiğini ortaya koymuştur. Ayrıca, hassasiyet, geri çağırma ve F1-skoru ölçümleri tüm sınıflarda güçlü performans gösterirken, VGG16 ve ResNet50 özellikle yüksek puanlar elde etmiştir. Binalarda fiziksel hasar tespiti için derin öğrenme yöntemlerinin etkinliği gösterilmiş ve transfer öğrenme yöntemlerinin karşılaştırmalı performansına ilişkin içgörüler sağlanmıştır.

Anahtar Kelimeler:

Yapısal hasar sınıflandırması, derin öğrenme, evrişimli sinir ağları, transfer öğrenme.

Deep Learning for Physical Damage Detection in Buildings: A Comparison of Transfer Learning Methods

The detection of physical damage in buildings is a critical task in ensuring the safety and integrity of structures. In this study, the effectiveness of deep learning methods for detecting physical damage in buildings, specifically focusing on cracks, defects, moisture, and undamaged classes was investigated. Transfer learning methods, including VGG16, GoogLeNet, and ResNet50, were used to classify a dataset of 7200 images. The dataset was split into training, validation, and testing sets, and the performance of the models was evaluated by using metrics such as accuracy, precision, recall, and F1-score. Results show that all three models achieved high accuracy on the test set, with VGG16 and ResNet50 outperforming GoogLeNet. Additionally, precision, recall, and F1-score metrics indicate strong performance across all classes, with VGG16 and ResNet50 achieving particularly high scores. It is demonstrated the effectiveness of deep learning methods for physical damage detection in buildings and provides insights into the comparative performance of transfer learning methods.

Keywords:

Structural damage classification, deep learning, convolutional neural networks, transfer learning.,

Tam Metin

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Bibtex	@araştırma makalesi { tjst1291814, journal = {Turkish Journal of Science and Technology}, issn = {1308-9080}, eissn = {1308-9099}, address = {fenbilimdergi@firat.edu.tr}, publisher = {Fırat Üniversitesi}, year = {2023}, volume = {18}, number = {2}, pages = {291 - 299}, doi = {10.55525/tjst.1291814}, title = {Deep Learning for Physical Damage Detection in Buildings: A Comparison of Transfer Learning Methods}, key = {cite}, author = {Bektaş Ekici, Betül and Ustaoğlu, Saltuk Taha} }
APA	Bektaş Ekici, B. & Ustaoğlu, S. T. (2023). Deep Learning for Physical Damage Detection in Buildings: A Comparison of Transfer Learning Methods . Turkish Journal of Science and Technology , 18 (2) , 291-299 . DOI: 10.55525/tjst.1291814
MLA	Bektaş Ekici, B. , Ustaoğlu, S. T. "Deep Learning for Physical Damage Detection in Buildings: A Comparison of Transfer Learning Methods" . Turkish Journal of Science and Technology 18 (2023 ): 291-299 <
Chicago	Bektaş Ekici, B. , Ustaoğlu, S. T. "Deep Learning for Physical Damage Detection in Buildings: A Comparison of Transfer Learning Methods". Turkish Journal of Science and Technology 18 (2023 ): 291-299
RIS	TY - JOUR T1 - Binalarda Fiziksel Hasar Tespiti için Derin Öğrenme: Transfer Öğrenme Yöntemlerinin Karşılaştırılması AU - BetülBektaş Ekici, Saltuk TahaUstaoğlu Y1 - 2023 PY - 2023 N1 - doi: 10.55525/tjst.1291814 DO - 10.55525/tjst.1291814 T2 - Turkish Journal of Science and Technology JF - Journal JO - JOR SP - 291 EP - 299 VL - 18 IS - 2 SN - 1308-9080-1308-9099 M3 - doi: 10.55525/tjst.1291814 UR - Y2 - 2023 ER -
EndNote	%0 Turkish Journal of Science and Technology Deep Learning for Physical Damage Detection in Buildings: A Comparison of Transfer Learning Methods %A Betül Bektaş Ekici , Saltuk Taha Ustaoğlu %T Deep Learning for Physical Damage Detection in Buildings: A Comparison of Transfer Learning Methods %D 2023 %J Turkish Journal of Science and Technology %P 1308-9080-1308-9099 %V 18 %N 2 %R doi: 10.55525/tjst.1291814 %U 10.55525/tjst.1291814
ISNAD	Bektaş Ekici, Betül , Ustaoğlu, Saltuk Taha . "Deep Learning for Physical Damage Detection in Buildings: A Comparison of Transfer Learning Methods". Turkish Journal of Science and Technology 18 / 2 (Eylül 2023): 291-299 .
AMA	Bektaş Ekici B. , Ustaoğlu S. T. Deep Learning for Physical Damage Detection in Buildings: A Comparison of Transfer Learning Methods. TJST. 2023; 18(2): 291-299.
Vancouver	Bektaş Ekici B. , Ustaoğlu S. T. Deep Learning for Physical Damage Detection in Buildings: A Comparison of Transfer Learning Methods. Turkish Journal of Science and Technology. 2023; 18(2): 291-299.
IEEE	B. Bektaş Ekici ve S. T. Ustaoğlu , "Deep Learning for Physical Damage Detection in Buildings: A Comparison of Transfer Learning Methods", , c. 18, sayı. 2, ss. 291-299, Eyl. 2023, doi:10.55525/tjst.1291814