3D tools for building and infrastructures inspection from thermal UAS data: first steps
Procedia Structural Integrity, ISSN: 2452-3216, Vol: 42, Page: 1121-1127
2022
- 1Citations
- 5Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
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Article Description
Thermal imaging has been widely used for the inspection of human-made and natural scenarios, from electrical installations and advanced machinery to buildings. Regarding buildings, it can be used to assess problems concerning heat, airflows, and water. Hence, thermography has been increasingly applied to the energy efficiency of new and old buildings. Some examples of applications are the detection of heating and cooling losses, moisture sources, missing insulation, floor heating failures, and the evaluation of building restorations. However, the main drawback of thermal imagery is its reduced resolution, e.g., 640 × 512, which is significantly lower than RGB imagery. In the case of buildings, cameras or thermal sensors mounted on UAS (Unmanned Aerial System) offer a wider range of possibilities by acquiring areas not accessible by other surveying methods. This paper presents a tool that allows building dense 3D thermal point clouds applied to conserving buildings and infrastructures inspection. Also, the aim of this work is to provide a visualization solution of the generated point clouds, thus allowing a human operator to analyze building defects. Accordingly, the rendering is improved by taking advantage of modern Graphical Processing Unit (GPU) capabilities, while also considering the occlusion for the accurate assignment of thermal information to the point cloud. Due to the high response time of the procedure, the complete pipeline is accelerated using GPU programming. Finally, our method is proven to generate point clouds with a higher number of points and density than notable commercial solutions, while also lowering the response time.
Bibliographic Details
http://www.sciencedirect.com/science/article/pii/S2452321622007004; http://dx.doi.org/10.1016/j.prostr.2022.12.143; http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85159018888&origin=inward; https://linkinghub.elsevier.com/retrieve/pii/S2452321622007004; https://dx.doi.org/10.1016/j.prostr.2022.12.143
Elsevier BV
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