Document Type : Research Paper

Authors

1 Dept. of Surveying and Geomatics Engineering - College of Engineering University of Tehran

2 Dept. of Photogrammetry, School of Surveying and Geospatial Engineering, University College of Engineering, University of Tehran, Tehran, Iran

Abstract

Extended Abstract
Introduction, Materials
Improving energy efficiency in buildings has become a major topic of interest in recent studies. Modern technologies have improved energy performance in new buildings. However, there is a growing demand for inspecting old buildings and enhancing their energy efficiency. Areas of heat dissipation are the most significant faults in insulation occurring as a result of thermal bridge, excessive heat loss, air leakage, or defective thermal insulation in building components. Heat dissipation mainly occurs on the facade. Lack of sufficient information on the energy performance and associated costs of retrofitting buildings have made visualization and determination of the heat dissipation areas crucial for improving energy efficiency. The present study primarily seeks to determine areas of heat dissipation on building facades in order to optimize energy efficiency and energy storage in buildings. A vertical flight Unmanned Aerial Vehicle (UAVs) with low altitude flight, equipped with Post-Processing Kinematic (PPK) module and MC1-640s thermal infrared camera made by KeiiElectro Optics Technology at a rate of 30 frames per second have been utilized in the present study to gather the needed data. Also, thermal infrared images of the building facade were collected from PedarSalar palace in Aliabad village, Aradan-Garmsar city with a longitude of 52.3034 and a latitude of 35.1600 in order to assess the proposed method.
 
Methods, Results
The present study seeks to propose a method for visualizing and determining the heat dissipation areas in facades with the aim of increasing energy efficiency. The proposed research method was divided into two parts. The first stage involved the generation of a dense point cloud and related orthophotomosaics utilizing thermal infrared images collected by UAVs, bundles adjustment, Structure from Motion (SfM) and Multi View Stereo (MVS) algorithms. The second stage involved converting the thermal infrared orthophotomosaic to HSV color space in order to choose the seed pixels for the Region-Growing-based segmentation algorithm. Since Hue-Saturation-Value (HSV) color space performs better when visualizing the concept of light, seed pixels were chosen from the HSV color space pixels with the highest degrees of grayscale to enter the segmentation algorithm. Then, introducing the seed pixels as input to the Region-Growing algorithm, areas of heat dissipation were automatically determined in the facade.
A dense thermal infrared point cloud was produced with a density of 1779067 points per square meter, Reprojection error of 0.41 pixels and Ground Sample Distance (GSD) of 0.75 cm using 45 thermal infrared images captured by UAVs flying perpendicular to the facade of the building at a distance of 11 meters and a flight altitude of 1.70 meters. The Precision and Recall evaluation criteria have been employed to analyze detected areas of heat dissipation. Precision and recall evaluation criteria equaled 90 percent and 87 percent, respectively. Results indicated that the proposed method has improved precision and recall evaluation criteria and determined areas of heat dissipation with higher accuracy.
 
Discussion, Conclusion
Given the critical importance of improving energy efficiency, and potential energy storage and reducing energy consumption in buildings and costs of production, obtaining related data to find optimization solutions is critical especially in older buildings. Since heat dissipation mainly occurs on the facade, the present study seeks to identify and determine areas of heat dissipation on the facade to visualize and improve energy efficiency applying the Region-Growing segmentation algorithm on the thermal infrared orthophotomosaic generated by photogrammetry UAVs. Since the HSV color space shows higher resolution in distribution of pixels used to extract areas of high temperature, seed pixels were introduced to the Region-Growing segmentation algorithm. Finally, precision and recall evaluation criteria were used to determine the accuracy of heat dissipation areas automatically detected on orthophotomosaics. Thus, the accuracy of the proposed method has been evaluated using the precision and recall criteria resulting in 90% and 87 %, respectively. Results indicated increased accuracy of the proposed heat dissipation detection method as compared to previous studies.

Keywords

1- Asdrubali, F., Baldinelli, G., & Bianchi, F. (2012). A quantitative methodology to evaluate thermal bridges in buildings. Applied Energy, 97, 365-373. doi:https://doi.org/10.1016/j.apenergy.2011.12.054
2- Baden, S., Fairey, P., Waide, P., de T’serclaes, P., & Laustsen, J. (2006). Hurdling financial barriers to low energy buildings: experiences from the USA and Europe on financial incentives and monetizing building energy savings in private investment decisions. Paper presented at the Proceedings of.
3- Baktykerey, A., & Zhanaliyev, A. (2020). Thermal vision camera equipped drone for predictive maintenance of grid sub-stations.
4- Barreira, E., Almeida, R. M. S. F., & Delgado, J. M. P. Q. (2016). Infrared thermography for assessing moisture related phenomena in building components. Construction and Building Materials, 110, 251-269. doi:https://doi.org/10.1016/j.conbuildmat.2016.02.026
5- Barreira, E., & de Freitas, V. P. (2007). Evaluation of building materials using infrared thermography. Construction and Building Materials, 21(1), 218-224. doi:https://doi.org/10.1016/j.conbuildmat.2005.06.049
6- Barreira, E., & Freitas, V. (2005). Importance of thermography in the study of ETICS finishing coatings degradation due to algae and mildew growth. Paper presented at the 10DBMC International Conference On Durability of Building Materials and Components.
7- Crandall, D., Owens, A., Snavely, N., & Huttenlocher, D. (2011, 20-25 June 2011). Discrete-continuous optimization for large-scale structure from motion. Paper presented at the CVPR 2011.
8- Danielski, I., & Fröling, M. (2015). Diagnosis of buildings’ thermal performance-a quantitative method using thermography under non-steady state heat flow. Energy Procedia, 83, 320-329.
9- Dlesk, A., & Vach, K. (2019). Point Cloud Generation of a Building from Close Range Thermal Images. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 42, 29-33.
10- Edelman, G., & Aalders, M. (2018). Photogrammetry using visible, infrared, hyperspectral and thermal imaging of crime scenes. Forensic science international, 292, 181-189.
11- Furukawa, Y., & Ponce, J. (2010). Accurate, Dense, and Robust Multiview Stereopsis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(8), 1362-1376. doi:10.1109/TPAMI.2009.161
12- Gonzalez, R. C., Eddins, S. L., & Woods, R. E. (2004). Digital image publishing using MATLAB: Prentice Hall.
13- Gorzalka, P., Estevam Schmiedt, J., Dahlke, D., Frommholz, D., Göttsche, J., Hoffschmidt, B., . . . Plattner, S. (2018). Building Tomograph–From Remote Sensing Data of Existing Buildings to Building Energy Simulation Input.
14- Hoegner, L., & Stilla, U. (2016). Automatic 3D reconstruction and texture extraction for 3D building models from thermal infrared image sequences. Quant. InfraRed Thermogr.
15- Hou, Y., Soibelman, L., Volk, R., & Chen, M. (2019). Factors affecting the performance of 3D thermal mapping for energy audits in a district by using infrared thermography (IRT) mounted on unmanned aircraft systems (UAS). Paper presented at the Proceedings of the 36th International Symposium on Automation and Robotics in Construction (ISARC).
16- Iwaszczuk, D., & Stilla, U. (2017). Camera pose refinement by matching uncertain 3D building models with thermal infrared image sequences for high quality texture extraction. ISPRS Journal of Photogrammetry and Remote Sensing, 132, 33-47. doi:https://doi.org/10.1016/j.isprsjprs.2017.08.006
17- Kakillioglu, B., Velipasalar, S., & Rakha, T. (2018b). Autonomous Heat Leakage Detection from Unmanned Aerial Vehicle-Mounted Thermal Cameras. Paper presented at the Proceedings of the 12th International Conference on Distributed Smart Cameras, Eindhoven, Netherlands. https://doi.org/10.1145/3243394.3243696
18- Khaloo, A., Lattanzi, D., Cunningham, K., Dell’Andrea, R., & Riley, M. (2018). Unmanned aerial vehicle inspection of the Placer River Trail Bridge through image-based 3D modelling. Structure and Infrastructure Engineering, 14(1), 124-136.
19- Kylili, A., Fokaides, P. A., Christou, P., & Kalogirou, S. A. (2014). Infrared thermography (IRT) applications for building diagnostics: A review. Applied Energy, 134, 531-549. doi:https://doi.org/10.1016/j.apenergy.2014.08.005
20- Lagüela, S., Díaz-Vilariño, L., Armesto, J., & Arias, P. (2014). Non-destructive approach for the generation and thermal characterization of an as-built BIM. Construction and Building Materials, 51, 55-61. doi:https://doi.org/10.1016/j.conbuildmat.2013.11.021
21- Lin, D., Jarzabek-Rychard, M., Schneider, D., & Maas, H.-G. (2018). THERMAL TEXTURE SELECTION AND CORRECTION FOR BUILDING FACADE INSPECTION BASED ON THERMAL RADIANT CHARACTERISTICS. International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences, 42(2).
22- López-Fernández, L., Lagüela, S., González-Aguilera, D., & Lorenzo, H. (2017). Thermographic and mobile indoor mapping for the computation of energy losses in buildings. Indoor and Built Environment, 26(6), 771-784.
23- Lowe, D. G. (2004). Distinctive image features from scale-invariant keypoints. International journal of computer vision, 60(2), 91-110.
24- Plesu, R., Teodoriu, G., & Taranu, G. (2012). Infrared thermography applications for building investigation. Buletinul Institutului Politehnic Din Lasi. Sectia Constructii, Arhitectura, 58(1), 157.
25- Rakha, T., Liberty, A., Gorodetsky, A., Kakillioglu, B., & 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), 30-44.
26- Shapiro, I. (2011). 10 common problems in energy audits. ASHRAE Journal, 53(2), 26-31.
27- Sledz, A., Unger, J., & Heipke, C. (2020). UAV-based thermal anomaly detection for distributed heating networks. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 43, 499-505.
28- Slocum, R. K., & Parrish, C. E. (2017). Simulated imagery rendering workflow for UAS-based photogrammetric 3D reconstruction accuracy assessments. Remote Sensing, 9(4), 396.
29- Szeliski, R. (2010). Computer vision: algorithms and applications: Springer Science & Business Media.
30- Vorajee, N., Mishra, A. K., & Mishra, A. K. (2020). Analyzing capacity of a consumer-grade infrared camera in South Africa for cost-effective aerial inspection of building envelopes. Frontiers of Architectural Research, 9(3), 697-710. doi:https://doi.org/10.1016/j.foar.2020.05.004
31- Watanabe, Y., & Kawahara, Y. (2016). UAV Photogrammetry for Monitoring Changes in River Topography and Vegetation. Procedia Engineering, 154, 317-325. doi:https://doi.org/10.1016/j.proeng.2016.07.482
32- Zhong, Y., Xu, Y., Wang, X., Jia, T., Xia, G., Ma, A., & Zhang, L. (2019). Pipeline leakage detection for district heating systems using multisource data in mid- and high-latitude regions. ISPRS Journal of Photogrammetry and Remote Sensing, 151, 207-222. doi:https://doi.org/10.1016/j.isprsjprs.2019.02.021
33- Zundel, S., & Stieß, I. (2011). Beyond profitability of energy-saving measures—attitudes towards energy saving. Journal of Consumer Policy, 34(1), 91-105.