Drone-Assisted Image Processing Scheme using Frame-Based Location Identification for Crack and Energy Loss Detection in Building Envelopes

This paper presents improved methods to detect cracks and thermal leakage in building envelopes using unmanned aerial vehicles (UAV) (i.e., drones) with video camcorders and/or infrared cameras. Three widely used contour detectors of Sobel, Laplacian, and Canny algorithms were compared to find a bet...

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Bibliographic Details
Published inEnergies (Basel) Vol. 14; no. 19; p. 6359
Main Authors Oh, Sukjoon, Ham, Suyeon, Lee, Seongjin
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.10.2021
Subjects
Anomalies
Automation
Bridges
Building envelopes
building thermal leakage detection
Camcorders
Cameras
Computer applications
Concrete
contour detection
crack inspection
Cracks
Drone aircraft
Drone vehicles
Drones
Energy
energy audit
Energy loss
Envelopes
Flaw detection
frame-based location identification
Image processing
Infrared cameras
Methods
Photogrammetry
Software
Unmanned aerial vehicles
Urban environments
Wavelet transforms
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Summary:This paper presents improved methods to detect cracks and thermal leakage in building envelopes using unmanned aerial vehicles (UAV) (i.e., drones) with video camcorders and/or infrared cameras. Three widely used contour detectors of Sobel, Laplacian, and Canny algorithms were compared to find a better solution with low computational overhead. Furthermore, a scheme using frame-based location identification was developed to effectively utilize the existing approach by finding the current location of the drone-assisted image frame. The results showed a simplified drone-assisted scheme along with automation, higher accuracy, and better speed while using lower battery energy. Furthermore, this paper found that the cost-effective drone with the attached equipment generated accurate results without using an expensive drone. The new scheme of this paper will contribute to automated anomaly detection, energy auditing, and commissioning for sustainably built environments.
ISSN:1996-1073
1996-1073
DOI:10.3390/en14196359