Software for roof defects recognition on aerial photographs

The article presents information on software for roof defects recognition on aerial photographs, made with air drones. An areal image segmentation mechanism is described. It allows detecting roof defects - unsmoothness that causes water stagnation after rain. It is shown that HSV-transformation appr...

Full description

Saved in:
Bibliographic Details
Published in:Journal of physics. Conference series 2018-05, Vol.1015 (3), p.32152
Main Authors: Yudin, D, Naumov, A, Dolzhenko, A, Patrakova, E
Format: Article
Language:English
Subjects:
Aerial photography
Defects
Drone aircraft
Image segmentation
Inspection
Object recognition
Physics
Roofing
Roofs
Software
Stagnation
Utilities
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The article presents information on software for roof defects recognition on aerial photographs, made with air drones. An areal image segmentation mechanism is described. It allows detecting roof defects - unsmoothness that causes water stagnation after rain. It is shown that HSV-transformation approach allows quick detection of stagnation areas, their size and perimeters, but is sensitive to shadows and changes of the roofing-types. Deep Fully Convolutional Network software solution eliminates this drawback. The tested data set consists of the roofing photos with defects and binary masks for them. FCN approach gave acceptable results of image segmentation in Dice metric average value. This software can be used in inspection automation of roof conditions in the production sector and housing and utilities infrastructure.
ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/1015/3/032152