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.
|Journal||Journal of Physics: Conference Series|
|Publication status||Published - 22 May 2018|
|Event||International Conference on Information Technologiess in Business and Industry 2018, ITBI 2018 - Tomsk, Russian Federation|
Duration: 17 Jan 2018 → 20 Jan 2018