Effect of different imaging modalities on the performance of a CNN: An experimental study on damage segmentation in infrared, visible, and fused images of concrete structures

Pozzer, Sandra; Souza, Marcos Paulo Vieira de; Hena, Bata; Hesam, Setayesh; Rezayiye, Reza Khoshkbary; Azar, Ehsan Rezazadeh; López, Fernando; Maldague, Xavier

doi:10.1016/j.ndteint.2022.102709

Cited by 21 publications

(11 citation statements)

References 47 publications

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“…By combining visual and IRT images in hybrid images, crack detectability is enhanced while minimizing false positives. Pozzer et al [ 155 ] investigated semantic segmentation of common concrete defects using various imaging modes. They trained a pre-trained convolutional neural network (CNN) model through transfer learning to detect concrete defects, including cracks, spalling, and potential subsurface defects.…”

Section: Infrared Thermography Monitoringmentioning

confidence: 99%

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A Review on Concrete Structural Properties and Damage Evolution Monitoring Techniques

Zhang,

Peng,

Wen

et al. 2024

Sensors

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Concrete structures have emerged as some of the most extensively utilized materials in the construction industry due to their inherent plasticity and high-strength characteristics. However, due to the temperature fluctuations, humidity, and damage caused by human activities, challenges such as crack propagation and structural failures pose threats to the safety of people’s lives and property. Meanwhile, conventional non-destructive testing methods are limited to defect detection and lack the capability to provide real-time monitoring and evaluating of concrete structural stability. Consequently, there is a growing emphasis on the development of effective techniques for monitoring the health of concrete structures, facilitating prompt repairs and mitigation of potential instabilities. This paper comprehensively presents traditional and novel methods for concrete structural properties and damage evolution monitoring, including emission techniques, electrical resistivity monitoring, electromagnetic radiation method, piezoelectric transducers, ultrasonic techniques, and the infrared thermography approach. Moreover, the fundamental principles, advantages, limitations, similarities and differences of each monitoring technique are extensively discussed, along with future research directions. Each method has its suitable monitoring scenarios, and in practical applications, several methods are often combined to achieve better monitoring results. The outcomes of this research provide valuable technical insights for future studies and advancements in the field of concrete structural health monitoring.

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Section: Infrared Thermography Monitoringmentioning

confidence: 99%

“… Combining visible and IRT images: ( a , b ) Visible and a thermal reference image for calibration; ( c ) Superposition of calibrated reference images ( a , b ); ( d , e ) Visible and thermal image of concrete structure; ( f ) Superposition of visible and infrared images ( d , e ) [ 155 ] (Copyright 2022, Elsevier). …”

Section: Figurementioning

confidence: 99%

A Review on Concrete Structural Properties and Damage Evolution Monitoring Techniques

Zhang,

Peng,

Wen

et al. 2024

Sensors

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“…In Jang et al (2019), deep machine learning automates concrete crack detection. Researchers in Pozzer et al (2022) have proposed a method based on deep learning and the infrared effect of different imaging methods; an experimental study has been done on the classification of damage in infrared, visible, and melted images of concrete structures to classify the severity of asphalt pavement cracks. In similar research, using deep learning and infrared thermography, asphalt pavement crack intensity classification has been investigated (Liu et al, 2022).…”

Section: Related Work 21 Structural Health Monitoring Based On Classi...mentioning

confidence: 99%

“…These weaknesses can result in irreparable complications if not addressed. However, these damages can be avoided if the structure's health is continuously monitored, evaluated, and maintained (Pozzer et al ., 2022). When defects are detected using standard visual inspection methods and non-destructive testing, it may be too late to prevent asset damage.…”

Section: Introductionmentioning

confidence: 99%

A shallow 2D-CNN network for crack detection in concrete structures

Honarjoo,

Darvishan

2024

IJSI

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PurposeThis study aims to obtain methods to identify and find the place of damage, which is one of the topics that has always been discussed in structural engineering. The cost of repairing and rehabilitating massive bridges and buildings is very high, highlighting the need to monitor the structures continuously. One way to track the structure's health is to check the cracks in the concrete. Meanwhile, the current methods of concrete crack detection have complex and heavy calculations.Design/methodology/approachThis paper presents a new lightweight architecture based on deep learning for crack classification in concrete structures. The proposed architecture was identified and classified in less time and with higher accuracy than other traditional and valid architectures in crack detection. This paper used a standard dataset to detect two-class and multi-class cracks.FindingsResults show that two images were recognized with 99.53% accuracy based on the proposed method, and multi-class images were classified with 91% accuracy. The low execution time of the proposed architecture compared to other valid architectures in deep learning on the same hardware platform. The use of Adam's optimizer in this research had better performance than other optimizers.Originality/valueThis paper presents a framework based on a lightweight convolutional neural network for nondestructive monitoring of structural health to optimize the calculation costs and reduce execution time in processing.

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“…7 Also, Pozzer et al used a pre-trained convolutional neural network to detect spalling, cracks, and subsurface defects on concrete structures in thermal images. 8 Despite the benefits of using a deep learning approach to automate detection, many industries still do not fully take advantage of these techniques. One of the main issues in this slow transition is the need for more data for training deep learning models.…”

Section: Introductionmentioning

confidence: 99%

Thermographic augmentation of defects on thermal images using multi-modal data

Nooralishahi¹,

Laurent

López³

et al. 2023

Thermosense: Thermal Infrared Applications XLV

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In recent years, many studies have focused on using deep-learning approaches for automatic defect detection in the thermographic inspection of industrial and construction components. Deep Convolutional Neural Networks have proven to perform remarkably on thermal defect detection. However, their convergence and accuracy are heavily associated with having a large amount of training data to avoid overfitting and ensure reliable detection. Unfortunately, the number of available labeled thermal datasets for inspection-related applications is very limited. One of the practical approaches to address this issue is data augmentation. This paper proposes a novel approach for augmenting simulated thermal defects on regions of interest using coupled thermal and visible images. The visible images are employed to extract regions of interest in both modalities using a texture segmentation method. Later, the introduced method is used to augment thermal defects on thermal images.

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Effect of different imaging modalities on the performance of a CNN: An experimental study on damage segmentation in infrared, visible, and fused images of concrete structures

Cited by 21 publications

References 47 publications

A Review on Concrete Structural Properties and Damage Evolution Monitoring Techniques

A Review on Concrete Structural Properties and Damage Evolution Monitoring Techniques

A shallow 2D-CNN network for crack detection in concrete structures

Thermographic augmentation of defects on thermal images using multi-modal data

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