BUILDINGS, vol.13, no.12, pp.1-21, 2023 (Peer-Reviewed Journal)
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.