Surface Crack Detection Using Hierarchal Convolutional Neural Network

Agyemang, Davis Bonsu; Bader, Mohamed

doi:10.1007/978-3-030-29933-0_15

Cited by 4 publications

(6 citation statements)

References 11 publications

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“…Based on the results of previous research [ 44 , 58 , 59 , 60 , 62 ], a CNN is used to classify the images presented in this work. The CNN model was implemented with the Keras deep learning library [ 67 ] using the TensorFlow backend.…”

Section: Methodsmentioning

confidence: 99%

“…Furthermore, many of these first studies [ 43 , 61 ] assume a certain brick size to design a sliding window which excludes joints. Some more sophisticated architectures have been deployed to handle heterogeneity of masonry structures [ 62 ], but most studies have focused on a relatively narrow image domain (i.e., images from a single site or a few similar sites).…”

Section: Introductionmentioning

confidence: 99%

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Crack Detection in Images of Masonry Using CNNs

Hallee

Napolitano

Reinhart

et al. 2021

Sensors

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While there is a significant body of research on crack detection by computer vision methods in concrete and asphalt, less attention has been given to masonry. We train a convolutional neural network (CNN) on images of brick walls built in a laboratory environment and test its ability to detect cracks in images of brick-and-mortar structures both in the laboratory and on real-world images taken from the internet. We also compare the performance of the CNN to a variety of simpler classifiers operating on handcrafted features. We find that the CNN performed better on the domain adaptation from laboratory to real-world images than these simple models. However, we also find that performance is significantly better in performing the reverse domain adaptation task, where the simple classifiers are trained on real-world images and tested on the laboratory images. This work demonstrates the ability to detect cracks in images of masonry using a variety of machine learning methods and provides guidance for improving the reliability of such models when performing domain adaptation for crack detection in masonry.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Crack Detection in Images of Masonry Using CNNs

Hallee

Napolitano

Reinhart

et al. 2021

Sensors

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“…Besides, CNNs have also gradually used in building external wall defect detection. Agyemang and Bader applied a CNN for detecting cracks on the building external walls and assessing the defects therein [3]. Perez et al also used CNNs to detect the building defects [9].…”

Section: Cnn Use For Building Deterioration Detectionmentioning

confidence: 99%

“…ese assessments include lengthy site inspection for systematic recording of the building elements' physical condition on the basis of note-taking, photographs, drawings, and customer-supplied information [2], followed by analysis of the collected data and writing of a health assessment report of the building. e components of this report include the assessed building's current state, recent updates, maintenance and repair records, and future longterm repair cost estimates [3]. However, this approach is a time-, labor-, and cost-intensive process and can endanger the surveyors' health and safety, particularly when the building to be assessed is a mid-to high-rise structure.…”

Section: Introductionmentioning

confidence: 99%

Application of Deep Learning and Unmanned Aerial Vehicle on Building Maintenance

Kung

Pan

Wang

et al. 2021

Advances in Civil Engineering

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Several natural and human factors are responsible for the defacement of the external walls and tiles of buildings, and the related deterioration can be a public safety hazard. Therefore, active building maintenance and repair processes are essential for ensuring building sustainability. However, conventional inspection methods are time-, cost-, and labor-intensive processes. Therefore, herein, this study proposes a convolutional neural network (CNN) model for image-based automated detection and localization of key building defects (efflorescence, spalling, cracking, and defacement). Based on a pretrained CNN VGG-16 classifier, this model applies class activation mapping for object localization. After identifying its limitations in real-life applications, this study determined the model’s robustness and ability to accurately detect and localize defects in the external wall tiles of buildings. For real-time detection and localization, this study applied this model by using mobile devices and drones. The results show that the application of deep learning with UAV can effectively detect various kinds of external wall defects and improve the detection efficiency.

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“…As can be seen from the literature, a large number of previous studies have been dedicated to crack detection for concrete structures [10][11][12][13][14][15][16][17][18][19][20]. Only recently, there is an increasing focus on detecting other forms of damage including concrete spalling [21][22][23][24].…”

Section: Introductionmentioning

confidence: 99%

Image Processing‐Based Spall Object Detection Using Gabor Filter, Texture Analysis, and Adaptive Moment Estimation (Adam) Optimized Logistic Regression Models

Hoang

2020

Advances in Civil Engineering

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This study aims at proposing a computer vision model for automatic recognition of localized spall objects appearing on surfaces of reinforced concrete elements. The new model is an integration of image processing techniques and machine learning approaches. The Gabor filter supported by principal component analysis and k-means clustering is used for identifying the region of interest within an image sample. The binary gradient contour, gray level co-occurrence matrix, and color channels’ statistical measurements are employed to compute the texture of the extracted region of interest. Based on the computed texture-based features, the logistic regression model trained by the state-of-the-art adaptive moment estimation (Adam) is utilized to establish a decision boundary that delivers predictions on the status of “nonlocalized spall” and “localized spall.” Experimental results demonstrate that the newly developed model is able to achieve good detection accuracy with classification accuracy rate = 85.32%, precision = 0.86, recall = 0.79, negative predictive value = 0.85, and F1 score = 0.82. Thus, the proposed computer vision model can be helpful to assist decision makers in the task of the periodic survey of structure heath condition.

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Surface Crack Detection Using Hierarchal Convolutional Neural Network

Cited by 4 publications

References 11 publications

Crack Detection in Images of Masonry Using CNNs

Crack Detection in Images of Masonry Using CNNs

Application of Deep Learning and Unmanned Aerial Vehicle on Building Maintenance

Image Processing‐Based Spall Object Detection Using Gabor Filter, Texture Analysis, and Adaptive Moment Estimation (Adam) Optimized Logistic Regression Models

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