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

Abstract

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.

Keywords

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

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

Abstract

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.

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