Sealed-Crack Detection Algorithm Using Heuristic Thresholding Approach

Kamaliardakani, Mojtaba; Sun, Lu; Ardakani, Mostafa K.

doi:10.1061/(asce)cp.1943-5487.0000447

Cited by 94 publications

(39 citation statements)

References 32 publications

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“…Here, the maximum value of each kernel is considered and forwarded to the next layer. The main tasks performed in the max-pooling layers are: (1) down sampling the data obtained from the previous layer to reduce the dimensionality of the data; and (2) reducing the number of model parameters, reducing computational time, and improving the model generalizability.…”

Section: Max-pooling Layermentioning

confidence: 99%

“…Researchers extensively studied the development of vision-based approaches for concrete crack detection, and have proposed several widely used approaches. Early image processing methods for crack detection include edge detection [ 1 ], thresholding and segmentation [ 2 ], region growing [ 3 ], and peculation-based techniques [ 4 ]. Local information-based models use various filters, such as morphological [ 5 ], statistical [ 6 ], 2D matched [ 7 ], and median filters, as well as multi-scale line filters based on the Hessian matrix [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

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Performance Evaluation of Deep CNN-Based Crack Detection and Localization Techniques for Concrete Structures

Ali

Alnajjar

Jassmi

et al. 2021

Sensors

184

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This paper proposes a customized convolutional neural network for crack detection in concrete structures. The proposed method is compared to four existing deep learning methods based on training data size, data heterogeneity, network complexity, and the number of epochs. The performance of the proposed convolutional neural network (CNN) model is evaluated and compared to pretrained networks, i.e., the VGG-16, VGG-19, ResNet-50, and Inception V3 models, on eight datasets of different sizes, created from two public datasets. For each model, the evaluation considered computational time, crack localization results, and classification measures, e.g., accuracy, precision, recall, and F1-score. Experimental results demonstrated that training data size and heterogeneity among data samples significantly affect model performance. All models demonstrated promising performance on a limited number of diverse training data; however, increasing the training data size and reducing diversity reduced generalization performance, and led to overfitting. The proposed customized CNN and VGG-16 models outperformed the other methods in terms of classification, localization, and computational time on a small amount of data, and the results indicate that these two models demonstrate superior crack detection and localization for concrete structures.

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Section: Max-pooling Layermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Performance Evaluation of Deep CNN-Based Crack Detection and Localization Techniques for Concrete Structures

Ali

Alnajjar

Jassmi

et al. 2021

Sensors

184

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show abstract

“…For the pavement image after median filtering, the gradient operator and closing operator are appropriate for edge extraction and gap closure, which can better extract the skeleton of pavement cracks. e threshold method segments pavement cracks and background by setting static or dynamic thresholds, realizing the automatic extraction of cracks [10,11]. In 1979, Otsu proposed a classical threshold segmentation method based on a gray histogram [12].…”

Section: Introductionmentioning

confidence: 99%

An Advanced Otsu Method Integrated with Edge Detection and Decision Tree for Crack Detection in Highway Transportation Infrastructure

Han¹,

Deng

Dong

et al. 2021

Advances in Materials Science and Engineering

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The detection of various cracks on pavement surfaces has drawn more and more attention from pavement maintenance engineers. In the traditional pavement image segmentation, due to the small area of the pavement cracks, the gray level of crack pixels only accounts for a very small portion in the grayscale histogram, making it difficult to segment. This paper developed an improved Otsu method integrated with edge detection and a decision tree classifier for cracking identification in asphalt pavements. An image preprocessing approach including Gaussian function-based spatial filtering and top-hat transform is firstly proposed to reduce the influence of poor shading and lighting effects significantly. Four edge detection operators including Prewitt, Sobel, Gauss–Laplace (LoG), and Canny are evaluated. The Canny edge detection has demonstrated outstanding performance in crack detection; this algorithm helps to obtain more details of both cracks and noises. The Sobel and LoG operators show similar image segmentation and retain fewer noises. The decision tree classifier based on the ID3 algorithm can effectively classify different types of cracks including transverse, longitudinal, and block ones.

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“…In the early years, the common assumption for crack detection is that the crack pixels exhibit lower intensity than the background pixels. Intensity is the most original and intuitive properties of cracks in images, so it can be used to separate cracks from the background by setting a local or global threshold value 18,19 . These methods are computationally efficient and can obtain acceptable results on images with small noises.…”

Section: Introductionmentioning

confidence: 99%

Curve‐based crack detection using crack information gain

Chen

Huang

Weng

et al. 2021

Struct Control Health Monit

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Summary Crack detection provides valuable information concerning the location, extent, type, and severity for structural health monitoring of civil infrastructures. Due to the influence of uneven lighting and imaging noise, as well as the debris, various textures, and materials of the different civil structure, detecting cracks in the surface images of civil infrastructures remains challenging in that there are many false positives and defragmentation of the crack curve. In this paper, an information‐based crack detection method is developed to robustly characterize and detect cracks on a curve‐by‐curve basis. Crack information gain (CIG) is defined to characterize the information of a local patch's being cracked. With the proposed model of estimating crack depth, the information‐based crack descriptor CIG is calculated by statistically modeling the probabilistic distribution of crack depth. Secondly, crack curves of different saliency are progressively detected by iteratively searching the salient string of pixels, regardless of the small gaps between fragments of the crack curve. Finally, crack curves are validated by examining the variation of CIG along the crack curve. The experimental results on a diverse set of images of different civil infrastructures demonstrate the generalizability of the proposed method, and the overall performance on each dataset outperforms the state‐of‐the‐art available methods with 2.9% improvement. The proposed method has potential for the quantitative evaluation of cracks on a meaningful curve basis.

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Sealed-Crack Detection Algorithm Using Heuristic Thresholding Approach

Cited by 94 publications

References 32 publications

Performance Evaluation of Deep CNN-Based Crack Detection and Localization Techniques for Concrete Structures

Performance Evaluation of Deep CNN-Based Crack Detection and Localization Techniques for Concrete Structures

An Advanced Otsu Method Integrated with Edge Detection and Decision Tree for Crack Detection in Highway Transportation Infrastructure

Curve‐based crack detection using crack information gain

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