The crack detection algorithm of pavement image based on edge information

Yang, Chunde; Geng, Mingyue

doi:10.1063/1.5039097

Cited by 5 publications

(3 citation statements)

References 8 publications

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“…Since a road crack usually has a thin and long appearance, the most straightforward characteristic is its continuous property. As a result, edge detection based methods [7][8][9][10] are adopted. Among them, Sobel and Canny edge detector are commonly used.…”

Section: Introductionmentioning

confidence: 99%

“…After smoothing images using bidimensional empirical mode decomposition (BEMD), Ayenu and Attoh [7] adopted a Sobel operator for cracks extraction. In [8], Yang and Geng detected the edges based on single pixel percolation processing, which was improved by using the local difference between pixels in the fixed region. However, the main drawback of such algorithms is the sensitiveness of noise that results in a high false positive rate.…”

Section: Introductionmentioning

confidence: 99%

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Pavement crack detection using non‐local theory and iterative sampling

Wei

Sun

et al. 2021

IET Image Processing

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Crack is a common form of road distress and a key study of an intelligent transportation system. However, automatic pavement crack detection is a very challenging task due to noisy texture background, intensity inhomogeneity, and topology complexity. In this paper, a new pavement crack detection algorithm to address these issues is proposed. First, non‐local block matching strategy and local statistical mean are put together to generate the probability map of cracks, which has advantages on automatic threshold choosing and strong resistance to intensity inhomogeneity. Second, an iterative seed points sampling algorithm is proposed, which makes full use of the area and shape of connected regions where the seeds lie in, thus exploiting high reliable crack seeds for following curves extraction. Finally, a minimum spanning tree (MST) is adopted to connect points into crack curves and employ a crack growth method to find out the cracks, which is specified to deal with complex topology of cracks. For parameters, a robust and optimal parameters selection rule is obtained by data driven method. The algorithm is compared with other state‐of‐the‐art algorithms on two datasets. The experiment result shows that the proposed method has a better detection performance on F1$F1$‐measure score over other methods.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Pavement crack detection using non‐local theory and iterative sampling

Wei

Sun

et al. 2021

IET Image Processing

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show abstract

“…Crack detection algorithms can emphasize edges by applying filters in either the spatial or frequency domain. Even though use of edge detectors for crack detection goes back to the early 2000s [29], these methods have been used in the past and are still being used in recent studies because of their simplicity and pixel-based detection of cracks [15,28,[30][31][32][33][34][35][36][37][38][39][40][41][42]. Even in emerging applications of supervised machine learning methods, edge detectors are still considered in practice since they do not require expensive annotated training datasets.…”

Section: Introductionmentioning

confidence: 99%

Benchmarking Image Processing Algorithms for Unmanned Aerial System-Assisted Crack Detection in Concrete Structures

2019

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This paper summarizes the results of traditional image processing algorithms for detection of defects in concrete using images taken by Unmanned Aerial Systems (UASs). Such algorithms are useful for improving the accuracy of crack detection during autonomous inspection of bridges and other structures, and they have yet to be compared and evaluated on a dataset of concrete images taken by UAS. The authors created a generic image processing algorithm for crack detection, which included the major steps of filter design, edge detection, image enhancement, and segmentation, designed to uniformly compare different edge detectors. Edge detection was carried out by six filters in the spatial (Roberts, Prewitt, Sobel, and Laplacian of Gaussian) and frequency (Butterworth and Gaussian) domains. These algorithms were applied to fifty images each of defected and sound concrete. Performances of the six filters were compared in terms of accuracy, precision, minimum detectable crack width, computational time, and noise-to-signal ratio. In general, frequency domain techniques were slower than spatial domain methods because of the computational intensity of the Fourier and inverse Fourier transformations used to move between spatial and frequency domains. Frequency domain methods also produced noisier images than spatial domain methods. Crack detection in the spatial domain using the Laplacian of Gaussian filter proved to be the fastest, most accurate, and most precise method, and it resulted in the finest detectable crack width. The Laplacian of Gaussian filter in spatial domain is recommended for future applications of real-time crack detection using UAS.

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