To identify the defects illustrated on building facades by employing infrared thermography under shadow

Huang, Yishuo; Shih, Paul; Hsu, Keng-Tsang; Chiang, Chih‐Hung

doi:10.1016/j.ndteint.2020.102240

Cited by 12 publications

(11 citation statements)

References 9 publications

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“…Intensity inhomogeneity is supposed to be removed such that their impacts on image segmentation can be reduced to a minimum. Several researchers proposed to have the image model incorporating intensity inhomogeneity in a multiplicative way, and then, level set functions are introduced in an iterative scheme [1][2][3]. All of them proposed an assumption: the contributions of intensity inhomogeneity are presented at the pixel and its neighborhoods.…”

Section: Introductionmentioning

confidence: 99%

“…Zhang et al introduced the circle function with the specified radius p to indicate that the pixels in the specified circle all contribute to intensity inhomogeneity [1]. The Gaussian function with the fixed window sizes was introduced to approximate intensity inhomogeneity [2] [5]. Huang et al used the algorithm proposed by Zhang, and the Gaussian function was applied instead of using the circle function to approximate the shadow effects illustrated in the thermal infrared images [6].…”

Section: Introductionmentioning

confidence: 99%

“…The Gaussian function with the fixed window sizes was introduced to approximate intensity inhomogeneity [2] [5]. Huang et al used the algorithm proposed by Zhang, and the Gaussian function was applied instead of using the circle function to approximate the shadow effects illustrated in the thermal infrared images [6]. The processed results demonstrate that intensity inhomogeneity does exit and is supposed to be removed.…”

Section: Introductionmentioning

confidence: 99%

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Applying the modified adaptive window sizes into the image segmentation to identify defects from thermal infrared images covered with intensity inhomogeneity

Huang

Yang

Chiang

2023

Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2023

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Thermal infrared images have been widely employed to detect defections. However, it is challenging to identify the defects from thermal infrared images contaminated by shadows, noise, etc. Those factors do be recorded in the thermal infrared images such that the recorded pixels not only contain the surface temperatures but also have the impacts from the factors. Those factors illustrated in the recorded pixels can be called intensity inhomogeneity. Several researchers have reported that the multiplicative way is feasible to approximate intensity inhomogeneity. A gaussian function is introduced to make sure that the intensity inhomogeneity works on not only a particular pixel but also its neighborhoods contribute. Usually, the fixed window size illustrated in the Gaussian function is used for the whole image for simplification. Intensity inhomogeneity is not spread uniformly over the given image. For those areas with high-intensity inhomogeneity, the Gaussian function with larger window sizes is introduced; otherwise, the Gaussian function with smaller window sizes is supposed to apply to the low-intensity inhomogeneity areas. Adaptive window sizes were proposed such that each pixel will have the Gaussian function with the specified window sizes according to the amount of intensity inhomogeneity. However, the adaptive approach needs a lot of computation, so defect detection will be slowed down. This study proposes the algorithm to modify the algorithm of adaptive window sizes provide an efficient way for defect detection. The proposed approach is based on image entropy; the image entropy will have a bigger value while the intensity inhomogeneity is larger. The image entropy was classified such that limited window sizes were introduced. In doing so, intensity inhomogeneity can be approximated, and the results of image segmentation can identify the defect.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Applying the modified adaptive window sizes into the image segmentation to identify defects from thermal infrared images covered with intensity inhomogeneity

Huang

Yang

Chiang

2023

Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2023

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“…Solution is to adjust temperature interval of thermal camera to remove the shadow effect by algorithm. (Huang, Shih, Hsu, Chiang, 2020). Aerial 3D thermography is highly efficient for complex data collection of building.…”

Section: Introductionmentioning

confidence: 99%

Roof Leak Detection by Thermography of as-Built Bim

Zahradník

2022

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. The horizontal roofs of industrial buildings are the weakest part of a structure. Imperfect design of horizontal roofs causes roof leakage. For roofs with a large surface area, locating leakage points is very difficult. The problem is solved by aerial thermography using drones. Modern high-resolution thermal cameras make it possible to detect roof leaks. By processing the RGB camera and thermal camera data, a thermal mesh model is created for implementation into the BIM. Building information models are data managers throughout the life cycle of buildings. BIM helps prevent clashes, improve collaboration and record building information from IOT devices. For building maintenance, the results of thermography need to be implemented in BIM. BIM managers create a roof repair plan based on the severity of the roof leakage. This paper proposes a methodology to implement the results of roof leak detection, starting from data collection and continuing with the processing of geometric and thermographic data and finalizes with implementation.

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“…In fact, Tejedor et al [35] highlighted that most studies were qualitative, to discover heterogeneities due to anomalies (moisture, thermal bridges, cracks, air leakages, etc.) below the plaster [36][37][38][39][40] and to define the geometry of a masonry, among other purposes [41]. Besides this, international standards (i.e., ISO 6781:1983 [42]), UNE EN 13187:1998 [43], ASTM E1311 [44], ASTM E1862 [45], and guidelines (i.e., RESNET [32]) only recommend boundary conditions for the use of qualitative IRT tests to carry out energy audits in buildings [33,46,47].…”

Section: Introductionmentioning

confidence: 99%

Analysis of the Applicability of Non-Destructive Techniques to Determine In Situ Thermal Transmittance in Passive House Façades

et al. 2020

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Within the European framework, the passive house has become an essential constructive solution in terms of building efficiency and CO2 reduction. However, the main approaches have been focused on post-occupancy surveys, measurements of actual energy consumption, life-cycle analyses in dynamic conditions, using simulation, and the estimation of the thermal comfort. Few studies have assessed the in situ performance of the building fabric of passive houses. Hence, this paper explores the applicability of non-destructive techniques—heat flux meter (HFM) and quantitative infrared thermography (QIRT)—for assessing the gap between the predicted and actual thermal transmittance of passive house façades under steady-state conditions in the Mediterranean climate. Firstly, the suitability of in situ non-destructive techniques was checked in an experimental mock-up, and, subsequently, a detached house was tested in the real built environment. The findings revealed that both Non-Destructive Testing (NDT) techniques allow for the quantification of the gap between the design and the actual façades U-value of a new passive house before its operational stage. QIRT was faster than the HFM technique, although the latter was more accurate. The results will help practitioners to choose the most appropriate method based on environmental conditions, execution of the method, and data analysis.

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To identify the defects illustrated on building facades by employing infrared thermography under shadow

Cited by 12 publications

References 9 publications

Applying the modified adaptive window sizes into the image segmentation to identify defects from thermal infrared images covered with intensity inhomogeneity

Applying the modified adaptive window sizes into the image segmentation to identify defects from thermal infrared images covered with intensity inhomogeneity

Roof Leak Detection by Thermography of as-Built Bim

Analysis of the Applicability of Non-Destructive Techniques to Determine In Situ Thermal Transmittance in Passive House Façades

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