Multivariate Analysis of Concrete Image Using Thermography and Edge Detection

Kim, Bubryur; Choi, Se-Woon; Hu, Gang; Lee, Dong‐Eun; Juan, Ronnie O. Serfa

doi:10.3390/s21217396

Cited by 9 publications

(8 citation statements)

References 39 publications

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“…One way to assess the quality of an image is by using no-reference metrics, such as the Blind or No-Reference IQA technique called the Blind or Referenceless Image Spatial Quality Evaluator (BRISQUE). Numerous studies have assessed the effectiveness of BRISQUE techniques in evaluating the quality of medical images [27] and concrete crack images [33].…”

Section: Brisque Methodsmentioning

confidence: 99%

Threshold-Based BRISQUE-Assisted Deep Learning for Enhancing Crack Detection in Concrete Structures

Pennada,

Perry,

McAlorum

et al. 2023

J. Imaging

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Automated visual inspection has made significant advancements in the detection of cracks on the surfaces of concrete structures. However, low-quality images significantly affect the classification performance of convolutional neural networks (CNNs). Therefore, it is essential to evaluate the suitability of image datasets used in deep learning models, like Visual Geometry Group 16 (VGG16), for accurate crack detection. This study explores the sensitivity of the BRISQUE method to different types of image degradations, such as Gaussian noise and Gaussian blur. By evaluating the performance of the VGG16 model on these degraded datasets with varying levels of noise and blur, a correlation is established between image degradation and BRISQUE scores. The results demonstrate that images with lower BRISQUE scores achieve higher accuracy, F1 score, and Matthew’s correlation coefficient (MCC) in crack classification. The study proposes the implementation of a BRISQUE score threshold (BT) to optimise training and testing times, leading to reduced computational costs. These findings have significant implications for enhancing accuracy and reliability in automated visual inspection systems for crack detection and structural health monitoring (SHM).

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Section: Brisque Methodsmentioning

confidence: 99%

Threshold-Based BRISQUE-Assisted Deep Learning for Enhancing Crack Detection in Concrete Structures

Pennada,

Perry,

McAlorum

et al. 2023

J. Imaging

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“…To address the problem of high failure rates of electric heating devices (EORs) during rail transportation, thermal imaging was employed to detect EOR errors and malfunctions using SVM [21]. Thermal images of concrete structures were preprocessed to emphasize and detect cracks on the exterior walls of buildings; SVM was then used to compute and classify the visual characteristics of each region to achieve accurate crack detection [22].…”

Section: Related Work a Thermal-imaging-based Anomaly Detectionmentioning

confidence: 99%

CECvT: Initial Diagnosis of Anomalies in Thermal Images

Kim,

Hwang,

Kim

2023

IEEE Access

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Given the global competitive landscape, it is imperative that businesses maintain and manage their facilities continuously to enhance efficiency and productivity for sustaining competitiveness. Hence, a new hybrid model called contrast enhancement convolutional vision transformer (CECvT) was developed in this study that enables fault diagnosis without physical contact with factory equipment to ensure accurate initial fault detection without the risk of machine damage or interference. This model leverages thermal imaging as an apt source for early anomaly detection in equipment. A new contrast enhancement module employing contrast enhancement techniques was integrated to address the edge information loss when utilizing thermal images. Moreover, the network performance was enhanced by fusing the advantages of convolutional neural network (CNN) and Transformer models. Notably, the model design allows deriving detailed feature information necessary for the initial diagnostics by harnessing multiscale information to extract and concatenate features. The proposed method's performance was evaluated using the thermal imaging dataset provided by AI Hub. When juxtaposed with CNN, Transformer, and hybrid CNN-Transformer models, the proposed model demonstrated a superior accuracy of 96.17%. Furthermore, it achieved the most accurate diagnosis at the inception of abnormalities than the other networks. The proposed model thus has potential and is preferrable for various thermal-imaging-based fault diagnosis applications owing to its excellent performance and precision during initial diagnosis.

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“…Step 4. The edge approach identifies the most essential edge aspects of an image and serves as a filter to improve the image [6].…”

Section: Image Processingmentioning

confidence: 99%

“…Despite the existing protocols on detailed visual examinations for manual assessments of concrete, humans still use psychophysical measurements in evaluating image quality, which is based on the human perception of visual information [6]. Given these human constraints inherent in manual inspections, the results may be inefficient and cause serious problems that contribute to the continuous deterioration of structures.…”

Section: Introductionmentioning

confidence: 99%

An Automated Image-Based Multivariant Concrete Defect Recognition Using a Convolutional Neural Network with an Integrated Pooling Module

Kim

Choi

et al. 2022

Sensors

Self Cite

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Buildings and infrastructure in congested metropolitan areas are continuously deteriorating. Various structural flaws such as surface cracks, spalling, delamination, and other defects are found, and keep on progressing. Traditionally, the assessment and inspection is conducted by humans; however, due to human physiology, the assessment limits the accuracy of image evaluation, making it more subjective rather than objective. Thus, in this study, a multivariant defect recognition technique was developed to efficiently assess the various structural health issues of concrete. The image dataset used was comprised of 3650 different types of concrete defects, including surface cracks, delamination, spalling, and non-crack concretes. The proposed scheme of this paper is the development of an automated image-based concrete condition recognition technique to categorize, not only non-defective concrete into defective concrete, but also multivariant defects such as surface cracks, delamination, and spalling. The developed convolution-based model multivariant defect recognition neural network can recognize different types of defects on concretes. The trained model observed a 98.8% defect detection accuracy. In addition, the proposed system can promote the development of various defect detection and recognition methods, which can accelerate the evaluation of the conditions of existing structures.

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Multivariate Analysis of Concrete Image Using Thermography and Edge Detection

Cited by 9 publications

References 39 publications

Threshold-Based BRISQUE-Assisted Deep Learning for Enhancing Crack Detection in Concrete Structures

Threshold-Based BRISQUE-Assisted Deep Learning for Enhancing Crack Detection in Concrete Structures

CECvT: Initial Diagnosis of Anomalies in Thermal Images

An Automated Image-Based Multivariant Concrete Defect Recognition Using a Convolutional Neural Network with an Integrated Pooling Module

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