Deep Learning-Based Automated Detection of Cracks in Historical Masonry Structures

Hacıefendioğlu K., Altunışık A. C., Abdioğlu T.

BUILDINGS, cilt.13, sa.12, ss.1-21, 2023 (Hakemli Dergi)

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 13 Sayı: 12
  • Basım Tarihi: 2023
  • Doi Numarası: 10.3390/buildings13123113
  • Dergi Adı: BUILDINGS
  • Derginin Tarandığı İndeksler: ABI/INFORM, Aerospace Database, Applied Science & Technology Source, Business Source Elite, Business Source Premier, Communication Abstracts, Metadex, Public Affairs Index, Civil Engineering Abstracts
  • Sayfa Sayıları: ss.1-21
  • Karadeniz Teknik Üniversitesi Adresli: Evet

Özet

The efficient and precise identification of cracks in masonry stone structures caused by

natural or human-induced factors within a specific region holds significant importance in detecting

damage and subsequent secondary harm. In recent times, remote sensing technologies have been

actively employed to promptly identify crack regions during repair and reinforcement activities.

Enhanced image resolution has enabled more accurate and sensitive detection of these areas. This

research presents a novel approach utilizing deep learning techniques for crack area detection in

cellphone images, achieved through segmentation and object detection methods. The developed

model, named the CAM-K-SEG segmentation model, combines Grad-CAM visualization and K-Mean

clustering approaches with pre-trained convolutional neural network models. A comprehensive

dataset comprising photographs of numerous historical buildings was utilized for training the model.

To establish a comparative analysis, the widely used U-Net segmentation model was employed.

The training and testing datasets for the developed technique were meticulously annotated and

masked. The evaluation of the results was based on the Intersection-over-Union (IoU) metric values.

Consequently, it was concluded that the CAM-K-SEG model exhibits suitability for object recognition

and localization, whereas the U-Net model is well-suited for crack area segmentation.