TITLE
DETECTION OF BUILDING CRACKS FROM IMAGES IN CLOUD-FOG COMPUTING
AUTHOR(S)
Dušan Marković, Dejan Vujičić, Dijana Stojić, Uroš Pešović, Siniša Ranđić
ABSTRACT
Inspection of infrastructures, such as buildings, is significant to detect defects that can cause more damage. Finding defects, such as cracks on the building surface, timely represents information that helps to maintain stability, safety, and duration of the building. Certain parts of the building surface may be difficult to access manually. So, an automated system could be used with unmanned aerial vehicles (UAV) and computer vision techniques coupled with Convolutional Neural Networks (CNNs). Our work aims to present the process of training a neural network model for building crack detection on the Cloud with satisfactory accuracy. And then deploy that model at the Fog level for the new image classification. The subject of research is the distribution of processing tasks from a distance server or Cloud to the Fog node closer to the source to obtain the results of image processing without high value for delay, reduce data transmission to the Cloud platform, and thus reduce the network load and energy consumption on the Cloud.
DOI
PAGES
167-172
DOWNLOAD
https://unitechsp.tugab.bg/images/2023/4-KS/s5_p172_v1.pdf
How to cite this article:
Dušan Marković, Dejan Vujičić, Dijana Stojić, Uroš Pešović, Siniša Ranđić, DETECTION OF BUILDING CRACKS FROM IMAGES IN CLOUD-FOG COMPUTING, Proceedings of International Scientific Conference “UNITECH 2023” – Gabrovo, 167-172