Infrared thermography (IRT) is a matured tool, and it can be employed to monitor the health conditions of structures by measuring surface temperature information in real time and in a non-contact way. The surface temperature information provides an important clue to identifying the defects on the building exterior surfaces. According to the surface temperature measurements, for those parts covered by shadows, the surface temperature information is smaller than it is supposed to be. Similarly, glare effects in IRT can be defined as the excessive and uncontrolled brightness illustrated in IRT such that the surface temperature information is larger than it is supposed to be. In general, the shadow and glare effects are often introduced in the thermal images obtained using the passive IRT when the solar energy is the main heat source. The current study proposes an image model in a multiplicative way to evaluate the shadow and glare effects presented in IRT. The experimental results demonstrate that the proposed image model does efficiently remove the shadow or glare effects. A calibrated thermograph can be generated by introducing proper level set functions in the numerical model.
Defects presented on the facades of a building do have profound impacts on extending the life cycle of the building. How to identify the defects is a crucial issue; destructive and non-destructive methods are usually employed to identify the defects presented on a building. Destructive methods always cause the permanent damages for the examined objects; on the other hand, non-destructive testing (NDT) methods have been widely applied to detect those defects presented on exterior layers of a building. However, NDT methods cannot provide efficient and reliable information for identifying the defects because of the huge examination areas. Infrared thermography is often applied to quantitative energy performance measurements for building envelopes. Defects on the exterior layer of buildings may be caused by several factors: ventilation losses, conduction losses, thermal bridging, defective services, moisture condensation, moisture ingress, and structure defects. Analyzing the collected thermal images can be quite difficult when the spatial variations of surface temperature are small. In this paper the authors employ image segmentation to cluster those pixels with similar surface temperatures such that the processed thermal images can be composed of limited groups. The surface temperature distribution in each segmented group is homogenous. In doing so, the regional boundaries of the segmented regions can be identified and extracted. A terrestrial laser scanner (TLS) is widely used to collect the point clouds of a building, and those point clouds are applied to reconstruct the 3D model of the building. A mapping model is constructed such that the segmented thermal images can be projected onto the 2D image of the specified 3D building. In this paper, the administrative building in Chaoyang University campus is used as an example. The experimental results not only provide the defect information but also offer their corresponding spatial locations in the 3D model.