Automated Vision-Based Building Inspection Using Drone Thermography
Publication: Construction Research Congress 2022
ABSTRACT
Building energy retrofit has a significant role in achieving clean energy goals in the United States, creating a vast construction market. The initial step to improving energy efficiency is a detailed building inspection. However, building inspections are often time-consuming and not scalable. Therefore, the application of drone thermography, which can be used to create digital geometry, is explored for an automated building inspection. This application has the advantages of reducing time, labor, and safety concerns for inspection activities while solving problems of inaccessible building envelope components. Consequently, we used Infrared Thermal cameras installed on an Unmanned Aerial System to collect thermal images from building sites. The proposed workflow was started with designing a drone flight path for data collection, followed by implementing computer vision algorithms to analyze the data to identify thermal anomalies of building envelope. The paper examined a campus building in Syracuse, New York. Thermal anomalies related to cases of beam to wall thermal bridge, material degradation, and air leakage were successfully detected for the case study. Additionally, the proposed approach may be integrated with building simulation toward enriching building energy assessment. This study contributes to solve problems associated with productivity in the construction sector and to improve commercial inspection efficiency.
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REFERENCES
ANSI/ASHRAE/ACCA. (2018). Standard 211-2018, Standard for Commercial Building Energy Audits.
Asdrubali, F., Baldinelli, G., Bianchi, F., Costarelli, D., Rotili, A., Seracini, M., and Vinti, G. (2018). Detection of thermal bridges from thermographic images by means of image processing approximation algorithms. Applied Mathematics and Computation, 317, 160–171.
Baldinelli, G., Bianchi, F., Rotili, A., Costarelli, D., Seracini, M., Vinti, G., Asdrubali, F., and Evangelisti, L. (2018). A model for the improvement of thermal bridges quantitative assessment by infrared thermography. Applied Energy, 211, 854–864.
Bradski, G. (2000). The OpenCV Library. Dr. Dobb’s Journal of Software Tools.
Canny, J. (1986). A Computational Approach to Edge Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 6, 679–698.
Cho, Y. K., Ham, Y., and Golpavar-Fard, M. (2015). 3D as-is building energy modeling and diagnostics : A review of the state-of-the-art. Advanced Engineering Informatics, 29.
Federal Aviation Administration. (2021). Become a Drone Pilot. Retrieved from U.S. Department of Transportation website: https://www.faa.gov/uas/commercial_operators/become_a_drone_pilot/#:~:text=In order to fly your,procedures for safely flying drones.
Ficapal, A., and Mutis, I. (2019). Framework for the Detection, Diagnosis, and Evaluation of Thermal Bridges Using Infrared Thermography and Unmanned Aerial Vehicles. Buildings, 9(179).
De Filippo, M., Asadiabadi, S., Ko, N., and Sun, H. (2019). Concept of Computer Vision Based Algorithm for Detecting Thermal Anomalies in Reinforced Concrete Structures. 15th International Workshop on Advanced Infrared Technology and Applications (AITA 2019).
Garrido, I., Lagüela, S., Arias, P., and Balado, J. (2018). Thermal-based analysis for the automatic detection and characterization of thermal bridges in buildings. Energy & Buildings, 158, 1358–1367.
Garrido, I., Lagüela, S., and Arias, P. (2018). Autonomous thermography : towards the automatic detection and classification of building pathologies. 14th Quantitative InfraRed Thermography Conference Autonomous, (July). Berlin, Germany.
Harris, C. R., Millman, K. J., Van Der Walt, S. J., Gommers, R., Virtanen, P., Cournapeau, D., Wieser, E., Taylor, J., Berg, S., Smith, N. J., and Van Kerkwijk, M. H. (2020). Array programming with NumPy. Nature, 585, 357–362.
Hou, Y., Volk, R., Chen, M., and Soibelman, L. (2021). Fusing tie points ’ RGB and thermal information for mapping large areas based on aerial images : A study of fusion performance under different flight configurations and experimental conditions. Automation in Construction, 124, 103554.
Iwaszczuk, D., and Stilla, U. (2016). QUALITY ASSESSMENT OF BUILDING TEXTURES EXTRACTED FROM OBLIQUE AIRBORNE THERMAL IMAGERY. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, III.
Kim, C., Choi, J., Jang, H., and Kim, E. (2021). Automatic Detection of Linear Thermal Bridges from Infrared Thermal Images Using Neural Network. Applied Sciences, 11(931).
Kylili, A., Fokaides, P. A., Christou, P., and Kalogirou, S. A. (2014). Infrared thermography (IRT) applications for building diagnostics : A review. Applied Energy, 134, 531–549.
Lucchi, E. (2018). Applications of the infrared thermography in the energy audit of buildings : A review. Renewable and Sustainable Energy Reviews, 82(October 2017), 3077–3090.
Park, G., Lee, M., Jang, H., and Kim, C. (2021). Thermal anomaly detection in walls via CNN-based segmentation. Automation in Construction, 125, 103627.
Park, J., Kim, P., Cho, Y. K., and Kang, J. (2019). Framework for automated registration of UAV and UGV point clouds using local features in images. Automation in Construction, 98, 175–182. https://doi.org/10.1016/j.autcon.2018.11.024.
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., and Thirion, B. (2011). Scikit-learn : Machine Learning in Python. Journal of Machine Learning Research, 12, 2825–2830.
Rakha, T., and Gorodetsky, A. (2018). Review of Unmanned Aerial System (UAS) applications in the built environment : Towards automated building inspection procedures using drones. Automation in Construction, 93(May), 252–264.
Rakha, T., Liberty, A., Gorodetsky, A., Kakillioglu, B., and Velipasalar, S. (2018). Heat Mapping Drones : An Autonomous Computer-Vision-Based Procedure for Building Envelope Inspection Using Unmanned Aerial Systems (UAS). Technology|Architecture + Design, 2:1(September), 30–44.
Shapiro, I. (2009). Energy audits in large commercial office buildings. ASHRAE Journal, 51.
Taylor, T., Counsell, J., and Gill, S. (2013). Energy efficiency is more than skin deep : Improving construction quality control in new-build housing using thermography. Energy & Buildings, 66, 222–231.
UNEP (United Nation Environment Programme). (2017). Global status report: Towards a zero-emission, efficient, and resilient buildings and construction sector.
United States Department of Energy. (2015). Increasing Efficiency of Building Systems and Technologies. In Quadrennial Technology Review. The United States Department of Energy.
US Department of Energy. (2015). Chapter 5: Increasing Efficiency of Building Systems and Technologies. In Quadrennial Technology Review: An assessment of energy technologies and research opportunities. Retrieved from https://energy.gov/sites/prod/files/2015/09/f26/%0AQuadrennial-Technology-Review-2015_0.pdf.
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Published online: Mar 7, 2022
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