Automated data-processing technique: 2D Map for identifying the distribution of the U-value in building elements by quantitative internal thermography

Tejedor, Blanca; Barreira, Eva; Almeida, Ricardo M. S. F.; Casals, Miquel

doi:10.1016/j.autcon.2020.103478

Cited by 18 publications

(11 citation statements)

References 70 publications

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“…Moreover, in the RGB color model, the acquired image does not need any further transformation to display features of image texture. Therefore, the RGB color model is considered the default color model for most image applications [ 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 36 , 39 , 42 ], and researchers do not typically investigate other available color models. Our findings support the selection of the RGB color model as a default color model for the analysis of image texture of equine IRT images.…”

Section: Discussionmentioning

confidence: 99%

“…Fortunately, in recent times, digital image processing has been developed for more comprehensive IRT image analysis. Such image processing provides quantified, objective data considered to be more informative than conventional temperature measures in the fields of material mechanics [ 29 , 30 , 31 ] and human medicine [ 32 , 33 , 34 , 35 , 36 , 37 ]. Digital image processing has been applied successfully in the detection of damage to composite materials [ 29 ], aerospace structures [ 30 ], and building elements [ 31 ], thus increasing the efficiency of non-destructive material testing.…”

Section: Introductionmentioning

confidence: 99%

“…Such image processing provides quantified, objective data considered to be more informative than conventional temperature measures in the fields of material mechanics [ 29 , 30 , 31 ] and human medicine [ 32 , 33 , 34 , 35 , 36 , 37 ]. Digital image processing has been applied successfully in the detection of damage to composite materials [ 29 ], aerospace structures [ 30 ], and building elements [ 31 ], thus increasing the efficiency of non-destructive material testing. Similarly, increased efficiency has been reported in the detection of diabetes [ 32 ], skin cancer [ 33 ], and breast tumors [ 34 ].…”

Section: Introductionmentioning

confidence: 99%

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Selection of Image Texture Analysis and Color Model in the Advanced Image Processing of Thermal Images of Horses following Exercise

Domino

Borowska

Kozłowska

et al. 2022

Animals

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As the detection of horse state after exercise is constantly developing, a link between blood biomarkers and infrared thermography (IRT) was investigated using advanced image texture analysis. The aim of the study was to determine which combinations of RGB (red–green–blue), YUI (brightness–UV–components), YIQ (brightness–IQ–components), and HSB (hue–saturation–brightness) color models, components, and texture features are related to the blood biomarkers of exercise effect. Twelve Polish warmblood horses underwent standardized exercise tests for six consecutive days. Both thermal images and blood samples were collected before and after each test. All 144 obtained IRT images were analyzed independently for 12 color components in four color models using eight texture-feature approaches, including 88 features. The similarity between blood biomarker levels and texture features was determined using linear regression models. In the horses’ thoracolumbar region, 12 texture features (nine in RGB, one in YIQ, and two in HSB) were related to blood biomarkers. Variance, sum of squares, and sum of variance in the RGB were highly repeatable between image processing protocols. The combination of two approaches of image texture (histogram statistics and gray-level co-occurrence matrix) and two color models (RGB, YIQ), should be considered in the application of digital image processing of equine IRT.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Selection of Image Texture Analysis and Color Model in the Advanced Image Processing of Thermal Images of Horses following Exercise

Domino

Borowska

Kozłowska

et al. 2022

Animals

View full text Add to dashboard Cite

show abstract

“…DIP has been applied in the fields of material mechanics [ 22 , 23 , 24 ] and human medicine [ 25 , 26 , 27 , 28 , 29 , 30 ], and provides quantified, objective data considered to be more informative than conventional temperature measures. In materials science, DIP increased the efficiency of non-destructive materials testing, in the detection of damage to composite materials [ 22 ], aerospace structures [ 23 ], and building elements [ 24 ]. Moreover, in medical science, DIP increased the sensitivity and specificity of non-invasive diagnostic imaging, in the diagnosis of diabetes [ 25 ], skin cancer [ 26 ], and breast tumors [ 27 ].…”

Section: Introductionmentioning

confidence: 99%

Advances in Thermal Image Analysis for the Detection of Pregnancy in Horses Using Infrared Thermography

Domino

Borowska

Kozłowska

et al. 2021

Sensors

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Infrared thermography (IRT) was applied as a potentially useful tool in the detection of pregnancy in equids, especially native or wildlife. IRT measures heat emission from the body surface, which increases with the progression of pregnancy as blood flow and metabolic activity in the uterine and fetal tissues increase. Conventional IRT imaging is promising; however, with specific limitations considered, this study aimed to develop novel digital processing methods for thermal images of pregnant mares to detect pregnancy earlier with higher accuracy. In the current study, 40 mares were divided into non-pregnant and pregnant groups and imaged using IRT. Thermal images were transformed into four color models (RGB, YUV, YIQ, HSB) and 10 color components were separated. From each color component, features of image texture were obtained using Histogram Statistics and Grey-Level Run-Length Matrix algorithms. The most informative color/feature combinations were selected for further investigation, and the accuracy of pregnancy detection was calculated. The image texture features in the RGB and YIQ color models reflecting increased heterogeneity of image texture seem to be applicable as potential indicators of pregnancy. Their application in IRT-based pregnancy detection in mares allows for earlier recognition of pregnant mares with higher accuracy than the conventional IRT imaging technique.

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“…The image pre-processing includes the removal of unwanted background objects and the detection of inspected elements, such as windows, doors, walls, and other features. The current published research that addresses the terms of automating the methods for background removal, object detection, and U-value estimation is limited [ 43 , 44 , 45 ]. We, therefore, propose a fully automated three-layer framework for the U-value estimation of a building and its elements.…”

Section: Methodsmentioning

confidence: 99%

An Instance Segmentation and Clustering Model for Energy Audit Assessments in Built Environments: A Multi-Stage Approach

Arjoune

Peri

Sugunaraj

et al. 2021

Sensors

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Heat loss quantification (HLQ) is an essential step in improving a building’s thermal performance and optimizing its energy usage. While this problem is well-studied in the literature, most of the existing studies are either qualitative or minimally driven quantitative studies that rely on localized building envelope points and are, thus, not suitable for automated solutions in energy audit applications. This research work is an attempt to fill this gap of knowledge by utilizing intensive thermal data (on the order of 100,000 plus images) and constitutes a relatively new area of analysis in energy audit applications. Specifically, we demonstrate a novel process using deep-learning methods to segment more than 100,000 thermal images collected from an unmanned aerial system (UAS). To quantify the heat loss for a building envelope, multiple stages of computations need to be performed: object detection (using Mask-RCNN/Faster R-CNN), estimating the surface temperature (using two clustering methods), and finally calculating the overall heat transfer coefficient (e.g., the U-value). The proposed model was applied to eleven academic campuses across the state of North Dakota. The preliminary findings indicate that Mask R-CNN outperformed other instance segmentation models with an mIOU of 73% for facades, 55% for windows, 67% for roofs, 24% for doors, and 11% for HVACs. Two clustering methods, namely K-means and threshold-based clustering (TBC), were deployed to estimate surface temperatures with TBC providing consistent estimates across all times of the day over K-means. Our analysis demonstrated that thermal efficiency not only depended on the accurate acquisition of thermal images but also relied on other factors, such as the building geometry and seasonal weather parameters, such as the outside/inside building temperatures, wind, time of day, and indoor heating/cooling conditions. Finally, the resultant U-values of various building envelopes were compared with recommendations from the American Society of Heating, Refrigerating, and Air-conditioning Engineers (ASHRAE) building standards.

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Automated data-processing technique: 2D Map for identifying the distribution of the U-value in building elements by quantitative internal thermography

Cited by 18 publications

References 70 publications

Selection of Image Texture Analysis and Color Model in the Advanced Image Processing of Thermal Images of Horses following Exercise

Selection of Image Texture Analysis and Color Model in the Advanced Image Processing of Thermal Images of Horses following Exercise

Advances in Thermal Image Analysis for the Detection of Pregnancy in Horses Using Infrared Thermography

An Instance Segmentation and Clustering Model for Energy Audit Assessments in Built Environments: A Multi-Stage Approach

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