Comparison of Classification Models for Detection of Cracks in Building Surfaces After Earthquake

Year 2022, , 899 - 910, 30.12.2022

Abdullah Şener , Burhan Ergen

https://doi.org/10.21605/cukurovaumfd.1230792

Abstract

An earthquake is a natural disaster that causes loss of life and property. It is of great importance to make preparations to minimize the damage and loss of life after an earthquake. In the study, the detection of small or large cracks on the wall surfaces was performed using image classification methods, which are one of the most popular working topics in the computer field in recent years. A dataset of 40000 wall images with and without cracks was used for the study. DenseNet-201, VGG-19 and Xception models were used separately for the classification. Feature maps of the images were extracted using the models. In the next step, the classification processes were performed with DenseNet-201 with 99% accuracy, with VGG-19 with 94% accuracy, and with the Xception model with 99% accuracy. Considering the success in the classification processes, an alternative method that can be used in damage assessment is presented.

Keywords

Image classification, Artificial neural networks, Deep learning, DenseNet-201, VGG-19, Xception

Deprem Sonrası Bina Yüzeylerinde Meydana Gelen Çatlakların Tespitinde Sınıflandırma Modellerinin Karşılaştırılması

Abstract

Deprem, can ve mal kaybına neden olan bir doğal afettir. Deprem sonrası hasarların ve can kayıplarının en aza indirilebilmesi için ön hazırlıkların yapılması büyük önem taşımaktadır. Yapılan çalışmada duvar yüzeylerinde meydana gelen küçük veya büyük çaplı çatlakların tespit edilmesi işlemleri, son yıllarda bilgisayar alanında popüler çalışma konularından biri olan görüntü sınıflandırma yöntemleriyle gerçekleştirilmiştir. Çalışmada içerisinde çatlakların bulunduğu ve bulunmadığı 40000 duvar görüntülerinden oluşan veri seti kullanılmıştır. Sınıflandırma işlemlerinde DenseNet-201, VGG-19 ve Xception modelleri ayrı ayrı kullanılmıştır. Modeller kullanılarak görüntüler özellik haritaları çıkartılmıştır. Bir sonraki aşamada ise DenseNet-201 ile %99, VGG-19 ile %94 ve Xception modeli ile
%99 doğruluk oranı ile sınıflandırma işlemleri gerçekleştirilmiştir. Gerçekleştirilen sınıflandırma işlemlerindeki başarılar göz önüne alındığında hasar tespiti işlemlerinde kullanılabilecek alternatif bir yöntem sunulmaktadır.

Keywords

Görüntü sınıflandırma, Yapay sinir ağları, Derin öğrenme, DenseNet-201, Xception, VGG-19

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