Automated subsurface defects' detection using point cloud reconstruction from infrared images

Puliti, Marco; Montaggioli, Giovanni; Sabato, Alessandro

doi:10.1016/j.autcon.2021.103829

Cited by 35 publications

(8 citation statements)

References 38 publications

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“…In the articles [47][48][49], the topic of buildings' auscultation using infrared thermal imaging and computer vision to detect defects is discussed. The structural condition of buildings can be assessed using these technologies.…”

Section: Focus On the Defects' Detection Issue Using Infrared Thermal...mentioning

confidence: 99%

A Review of the Use of Thermal Imaging and Computer Vision for Pattern Recognition

Clero,

Nadour,

Ed-Diny

et al. 2023

Cryptography and Blockchain

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The use of thermal imaging and computer vision has seen a sharp increase in recent years. This paper presents the current state of the art concerning these fields. Thus, a review of research articles studying the combination of these technologies for pattern recognition is established. After presenting an overview of the topic, a bibliometric analysis is selected based on 1160 articles extracted from the Scopus database, which allows assessing the temporal evolution of the publications and the study of their most relevant sources, authors, keywords, trend topics, and publishing countries. Besides, a particular focus is given to the issue of defect detection using thermal imaging and computer vision. The related articles are analyzed and classified into their primary domains. The results confirm that combining thermal imaging with computer vision techniques has become commonly used in several fields. However, the defects detection case studies using these technologies are focused on a few application fields. Thus, more contributions are required regarding this specific issue in more innovative applications. This review will help to guide future research contributions within all fields where creative uses of infrared thermal imaging and computer vision can be developed to recognize different patterns and issues.

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Section: Focus On the Defects' Detection Issue Using Infrared Thermal...mentioning

confidence: 99%

A Review of the Use of Thermal Imaging and Computer Vision for Pattern Recognition

Clero,

Nadour,

Ed-Diny

et al. 2023

Cryptography and Blockchain

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“…With the popularity of 3D reconstruction technology, the surface reconstruction has been widely applied in various fields, such as the mapping [1], driverless [2], medical technology [3], robotics [4]. For the methods of reconstructing surfaces based on the 3D point cloud data, they can be generally divided into two schemes: the local methods and the global methods.…”

Section: Introductionmentioning

confidence: 99%

An Improved Poisson Surface Reconstruction Algorithm based on the Boundary Constraints

Liu¹,

Wang²,

Tahir³

et al. 2023

IJACSA

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The usage of the point cloud surface reconstruction to generate high-precision 3D models has been widely applied in various fields. In order to deal with the problems of insufficient accuracy, pseudo-surfaces and high time cost caused by the traditional surface reconstruction algorithms of the point cloud data, this paper proposes an improved Poisson surface reconstruction algorithm based on the boundary constraints. For large density point cloud data obtained from 3D laser scanning, the proposed method firstly uses an octree instead of the KD-tree to search the near neighborhood; then, it uses the Open Multi-Processing (OpenMP) to accelerate the normal estimation based on the moving least squares algorithm; meanwhile, the leastcost spanning tree is employed to adjust the consistency of the normal direction; and finally a screened Poisson algorithm with the Neumann boundary constraints is proposed to reconstruct the point cloud. Compared with the traditional methods, the experiments on three open datasets demonstrated that the proposed method can effectively reduce the generation of pseudosurfaces. The reconstruction time of the proposed algorithm is about 16% shorter than that of the traditional Poisson reconstruction algorithm, and produce better reconstruction results in the term of quantitative analysis and visual comparison.

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“…One of the most effective techniques that can be employed to detect subsurface damage causing heat loss is infrared thermography (IRT). Infrared (IR) images can show subsurface irregularities in the object using local difference in their thermal signatures 3 and it is gaining popularity to detect thermal anomalies 4,5 . Inspection techniques that rely on IR images to detect subsurface anomalies resulting in irregular heat signatures have numerous applications including for heat loss detection [6][7][8] .…”

Section: Introductionmentioning

confidence: 99%

Application of mask RCNN for localizing and quantifying areas of energy leak in buildings using infrared images

Dabetwar

Niezrecki

Sabato

2023

Health Monitoring of Structural and Biological Systems XVII

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As buildings age, energy begins to leak through various locations such as window seals, walls, subsurface cracks, and damaged areas, even in seemingly healthy structures. Most of the time, areas of energy loss remain undetected because they are not visible to the naked eye. Due to the increasing amount of energy lost through such areas and defects which has an impact on overall energy efficiency. However, infrared images (IR) can be used to detect energy leaks as well as identify subsurface damages. Infrared thermography (IRT) is a popular method for assessing the condition of buildings and infrastructures. While IRT can provide information about the location and severity of energy leaks, manually analyzing the collected data can be a cumbersome process. As a result, there is a need to automate the detection of the areas from where energy is lost. Image segmentation methods based on deep learning algorithms can effectively automate the inspection process. In this study, an approach based on a pre-trained mask region-based convolutional neural network (mask RCNN) is proposed for the first time in conjunction with IR images to localize and quantify areas of heat loss. Mask RCNN demonstrated significant accuracy in identifying the location and quantifying the size of the area of heat loss in inspected buildings with above 99% confidence.

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Automated subsurface defects' detection using point cloud reconstruction from infrared images

Cited by 35 publications

References 38 publications

A Review of the Use of Thermal Imaging and Computer Vision for Pattern Recognition

A Review of the Use of Thermal Imaging and Computer Vision for Pattern Recognition

An Improved Poisson Surface Reconstruction Algorithm based on the Boundary Constraints

Application of mask RCNN for localizing and quantifying areas of energy leak in buildings using infrared images

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