Evaluation of the temperature pattern of a complex body from thermal imaging and 3D information: A method and its MATLAB implementation

Abstract The standard setting of a camera used in Infrared thermography (IRT) is based on the choice of the same values of emissivity and distance for all pixels of a thermal image even if the emissivity depends on the relative position of camera and observed surface. Often this is not a problem. However, the resulting temperature pattern could be inadequate if a body having a complex shape is observed from strongly constrained positions. In order to face this issue, a procedure aimed at providing a correct temperature pattern by using 3D information related to a point cloud is proposed together with its MATLAB implementation (COMAP3 toolbox). For each pixel of a thermal image, the relative position of camera and observed surface is estimated, leading to pixel-specific values of emissivity and distance. The temperature obtained in this way is also mapped onto the point cloud. The effectiveness of the procedure in recognizing areas characterized by peculiar thermal behavior is shown in the case of a historic cylindrical masonry bell tower (Caorle’s bell tower, Venice, Italy).

[1]  Max A. Viergever,et al.  Mutual-information-based registration of medical images: a survey , 2003, IEEE Transactions on Medical Imaging.

[2]  Youngjib Ham,et al.  Real-Time Energy Audit of Built Environments: Simultaneous Localization and Thermal Mapping , 2018, Journal of Infrastructure Systems.

[3]  Antonio Galgaro,et al.  Integration of laser scanning and thermal imaging in monitoring optimization and assessment of rockfall hazard: a case history in the Carnic Alps (Northeastern Italy) , 2015, Natural Hazards.

[4]  H. Budzier,et al.  Calibration of uncooled thermal infrared cameras , 2015 .

[5]  T. Loarer,et al.  Modeling of the ITER-like wide-angle infrared thermography view of JET. , 2012, The Review of scientific instruments.

[6]  Xavier Maldague,et al.  Quantitative evaluation of optical lock-in and pulsed thermography for aluminum foam material , 2013 .

[7]  Eugenio Castelli,et al.  IRTROCK: A MATLAB toolbox for contactless recognition of surface and shallow weakness of a rock cliff by infrared thermography , 2012, Comput. Geosci..

[8]  Theodore E. Matikas,et al.  Combined use of thermography and ultrasound for the characterization of subsurface cracks in concrete , 2010 .

[9]  Giordano Teza,et al.  Morphological Analysis for Architectural Applications: Comparison between Laser Scanning and Structure-from-Motion Photogrammetry , 2016 .

[10]  Kozo Saito,et al.  IR self-referencing thermography for detection of in-depth defects , 2005 .

[11]  Yong K. Cho,et al.  As-Is 3D Thermal Modeling for Existing Building Envelopes Using a Hybrid LIDAR System , 2013 .

[12]  David Bečkovský,et al.  Application of infrared thermography for mapping open fractures in deep-seated rockslides and unstable cliffs , 2014, Landslides.

[13]  Giordano Teza,et al.  Geometric characterization of a cylinder-shaped structure from laser scanner data: Development of an analysis tool and its use on a leaning bell tower , 2013 .

[14]  Osama Moselhi,et al.  Multisensor Data Fusion for Bridge Condition Assessment , 2017 .

[15]  Giovanni Battista Barla,et al.  3D Laser scanner and thermography for tunnel discontinuity mapping , 2016 .

[16]  Giordano Teza THIMRAN: MATLAB Toolbox for Thermal Image Processing Aimed at Damage Recognition in Large Bodies , 2014 .

[17]  W. Minkina,et al.  Atmospheric transmission coefficient modelling in the infrared for thermovision measurements , 2016 .

[18]  Chengdong Wu,et al.  3D Temperature Distribution Model Based on Thermal Infrared Image , 2017, J. Sensors.

[19]  Julio Molleda,et al.  Infrared Thermography for Temperature Measurement and Non-Destructive Testing , 2014, Sensors.

[20]  Mani Golparvar-Fard,et al.  Three-Dimensional Thermography-Based Method for Cost-Benefit Analysis of Energy Efficiency Building Envelope Retrofits , 2015, J. Comput. Civ. Eng..