Detection of Damaged Structures Using Deep Learning Algorithms with UAV

Erdem Emin Maraş; Halil İbrahim Sarıyıldız

Makale detayları

Detection of Damaged Structures Using Deep Learning Algorithms with UAV

Yazarlar:

Erdem Emin Maraş | Halil İbrahim Sarıyıldız

Yayıncı:

Afyon Kocatepe University

Ana Başlıklar:

Fotogrametri ve Uzaktan Algılama

Dergi:

Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi Vol.23 Issue 2, pp.427-437

Belge Tipi:

Makale

Yayın Tarihi:

3/2023

Belge Dili:

Türkçe

Document ID (Org No.):

2001288778

pISSN:

2149-3367

Anahtar kelimeler:

Özet:

 In the study, damage detection was tried to be done using Mask Region-based Convolutional Neural Network Mask R-CNN model, which is a model of convolutional neural network architecture in deep learning algorithms in order to detect damaged structures quickly and reliably. The 7.0 magnitude earthquake zone, which occurred in Haiti in 2010, in which 316,000 people died and 280,000 buildings were damaged, was chosen as the training area. Unmanned aerial vehicle images with a resolution of 5 cm and 7 cm used in the study. In the first stage, the images were trained and tested in three classes as damaged, slightly damaged and undamage. In the test process with unmanned aerial vehicle data, the structures were found with a rate of 58.62 and the structures found were found with an overall accuracy of 83.53. In the second stage, the images were labeled in two classes as damaged and undamaged and training and testing was carried out. In the test process with unmanned aerial vehicle data, the structures were found with a rate of 74.50 and the structures found were classified with an overall accuracy of 95.12.

 In the study, damage detection was tried to be done using Mask Region-based Convolutional Neural Network Mask R-CNN model, which is a model of convolutional neural network architecture in deep learning algorithms in order to detect damaged structures quickly and reliably. The 7.0 magnitude earthquake zone, which occurred in Haiti in 2010, in which 316,000 people died and 280,000 buildings were damaged, was chosen as the training area. Unmanned aerial vehicle images with a resolution of 5 cm and 7 cm used in the study. In the first stage, the images were trained and tested in three classes as damaged, slightly damaged and undamage. In the test process with unmanned aerial vehicle data, the structures were found with a rate of 58.62 and the structures found were found with an overall accuracy of 83.53. In the second stage, the images were labeled in two classes as damaged and undamaged and training and testing was carried out. In the test process with unmanned aerial vehicle data, the structures were found with a rate of 74.50 and the structures found were classified with an overall accuracy of 95.12.

 In the study, damage detection was tried to be done using Mask Region-based Convolutional Neural Network Mask R-CNN model, which is a model of convolutional neural network architecture in deep learning algorithms in order to detect damaged structures quickly and reliably. The 7.0 magnitude earthquake zone, which occurred in Haiti in 2010, in which 316,000 people died and 280,000 buildings were damaged, was chosen as the training area. Unmanned aerial vehicle images with a resolution of 5 cm and 7 cm used in the study. In the first stage, the images were trained and tested in three classes as damaged, slightly damaged and undamage. In the test process with unmanned aerial vehicle data, the structures were found with a rate of 58.62 and the structures found were found with an overall accuracy of 83.53. In the second stage, the images were labeled in two classes as damaged and undamaged and training and testing was carried out. In the test process with unmanned aerial vehicle data, the structures were found with a rate of 74.50 and the structures found were classified with an overall accuracy of 95.12.

 Bu araştırmada, hasarlı yapıların hızlı ve güvenilir bir şekilde tespit edilebilmesi için derin öğrenme algoritmalarında evrişimsel sinir ağı mimarisinin bir modeli olan Mask Region-based Convolutional Neural Network Mask R-CNN modeli kullanılarak hasar tespiti yapılmaya çalışılmıştır. Eğitim alanı olarak 2010 yılında Haitide meydana gelen 316.000 insanın öldüğü, 280.000 civarı yapının hasar gördüğü 7.0 büyüklüğündeki deprem bölgesi seçilmiştir. Bölgede 5 cm ve 7 cm çözünürlüğe sahip insansız hava aracı görüntüleri çalışmada kullanılmıştır. Görüntüler ilk aşamada üç sınıfta hasarlı, az hasarlı ve hasarsız olarak eğitilip test edilmiştir. İnsansız hava aracı verileri ile yapılan test işleminde ise yapılar 58.62 oranıyla bulunmuş ve bulunan yapılar 83.53 genel doğrulukla sınıflandırılmıştır. İkinci aşamada görüntüler hasarlı ve hasarsız olarak iki sınıfta etiketlenip eğitim ve test işlemi gerçekleştirilmiştir. İnsansız hava aracı verileri ile yapılan test işleminde ise yapılar 74.50 oranıyla bulunmuş ve bulunan yapılar 95.12 genel doğruluk oranıyla sınıflandırılmıştır.

Makale detayları

Detection of Damaged Structures Using Deep Learning Algorithms with UAV

Yazarlar:

Erdem Emin Maraş | Halil İbrahim Sarıyıldız

Yayıncı:

Afyon Kocatepe University

Ana Başlıklar:

Fotogrametri ve Uzaktan Algılama

Dergi:

Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi Vol.23 Issue 2, pp.427-437

Belge Tipi:

Makale

Yayın Tarihi:

3/2023

Belge Dili:

Türkçe

Document ID (Org No.):

2001288778

pISSN:

2149-3367

Deep Learning

Remote Sensing

Convolutional Neural Network

UAVDerin Öğrenme

İHA

Uzaktan Algılama

Evrişimsel Sinir Ağı

Sayı

Makale detayları

Yazarlar: Erdem Emin Maraş | Halil İbrahim Sarıyıldız

Yayıncı: Afyon Kocatepe University

Ana Başlıklar: Fotogrametri ve Uzaktan Algılama

Dergi: Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi Vol.23 Issue 2, pp.427-437

Belge Tipi: Makale

Yayın Tarihi: 3/2023

Belge Dili: Türkçe

Document ID (Org No.): 2001288778

pISSN: 2149-3367

Sayı

Yazarlar:

Erdem Emin Maraş | Halil İbrahim Sarıyıldız

Yayıncı:

Afyon Kocatepe University

Ana Başlıklar:

Fotogrametri ve Uzaktan Algılama

Dergi:

Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi Vol.23 Issue 2, pp.427-437

Belge Tipi:

Makale

Yayın Tarihi:

3/2023

Belge Dili:

Türkçe

Document ID (Org No.):

2001288778

pISSN:

2149-3367