An Automatic Image Processing Algorithm Based on Crack Pixel Density for Pavement Crack Detection and Classification

Safaei, Nima; Smadi, Omar; Masoud, Ahmad A.; Safaei, Babak

doi:10.1007/s42947-021-00006-4

Cited by 52 publications

(20 citation statements)

References 49 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this section, the method in this paper is compared with existing methods using the Crack500 data set. Safaei et al [ 37 ] proposed a tile-based cracking method, applying local threshold technology to each tile. According to the spatial distribution characteristics of crack pixels, the tiles containing cracks are detected, and after fitting a curve, classify longitudinal and transverse cracks by setting the slope threshold, hereinafter referred to as method 1; Song et al [ 30 ] proposed a crack classification method based on a characterization algorithm.…”

Section: Resultsmentioning

confidence: 99%

A Pavement Crack Detection Method Based on Multiscale Attention and HFS

Shi

et al. 2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

To solve the problem of low detection accuracy due to the loss of detailed information when extracting pavement crack features in traditional U-shaped networks, a pavement crack detection method based on multiscale attention and hesitant fuzzy set (HFS) is proposed. First, the encoding-decoding structure is used to construct a pavement crack segmentation network, ResNeXt50 is used to extract features in the encoding stage, and a multiscale feature fusion module (MFF) is designed to obtain multiscale context information. Second, in the decoding stage, a high-efficiency dual attention module (EDA) is used to enhance the ability of capturing details of the cracks while suppressing background noise. Finally, the membership degree of the crack is calculated based on the advantages of the HFS in multiattribute decision-making to obtain the similarity of the crack, and the binary image after segmentation is judged by the hesitation fuzzy measure. The experiment was conducted on the public road crack dataset Crack500. In terms of segmentation performance, the evaluation indexes Intersection over Union (IoU), Precision, and Dice coefficients of the proposed network reached 55.56%, 74.26%, and 67.43%, respectively; in terms of classification performance, for transversal and longitudinal cracks, the classification accuracy was 84% ± 0.5%, while the block and the alligator were both 78% ± 0.5%. The experimental results prove that the crack details detected by the proposed method are more abundant, and the image detection effect of complex topological structures and small cracks are better.

show abstract

Section: Resultsmentioning

confidence: 99%

A Pavement Crack Detection Method Based on Multiscale Attention and HFS

Shi

et al. 2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

“…Safaei et al. [6] proposed a crack detection network with a tile‐based image processing method for applying a localized thresholding technique on each tile of a crack image and finding the crack images based on the spatial distribution.…”

Section: Introductionmentioning

confidence: 99%

“…KrakN [4] proposed a sliding window in a CNN architecture with a transfer learning framework, whereas Ali et al [5] proposed a customised CNN for crack detection in concrete structures and investigated the efficiency of the crack detection network based on the experiment results of the complexity, data size, and variance. Safaei et al [6] proposed a crack detection network with a tile-based image processing method for applying a localized thresholding technique on each tile of a crack image and finding the crack images based on the spatial distribution.…”

mentioning

confidence: 99%

AugMoCrack: Augmented morphological attention network for weakly supervised crack detection

Hong

Lee

Yoo

2022

Electronics Letters

View full text Add to dashboard Cite

Most crack-detection methods adopt a pixel-level segmentation-based approach, which requires considerable time and complexity to detect the pixel area of the crack. Unlike a pixel-level segmentation-based approach, in this paper, the authors proposed an AugMoCrack network, a bounding box-level crack detection approach for weakly supervised crack detection achieved by augmenting the training data with Poisson blending, as well as high-frequency discrete cosine transformbased features. The proposed AugMoCrack detects the box position of the crack object from a morphological perspective, such as neighbour connectivity within the crack pixels and crack-area fitting. Based on this perspective, the authors also proposed new morphological attention loss functions for considering the neighbour connectivity and the box area border. Specifically, the authors trained the AugMoCrack network using two datasets and verified its performance based on 591 validation images in the concrete crack dataset and 672 validation images in the Crack500 dataset. Compared with the baseline architecture, with the authors' proposed network, the authors achieved increases of approximately 4.5% points and 2.5% points in the mean average precision (mAP) in the concrete crack and Crack500 datasets, respectively. The proposed network also outperforms previous state-of-the-art crackdetection methods in a weakly supervised learning environment where the training data are insufficient.

show abstract

“…From collected images, cracks can be detected by applying various image processing methods often a ected by light, shadows, and noise; therefore, the actual image processing of complex road cracks makes a technical challenge for crack detection. Automated pavement crack detection based on image analysis includes image processing, machine learning, and deep learning approaches [5][6][7][8]. In the case of image processing, methods are mainly based on texture analysis, including image thresholding or edge detection techniques.…”

Section: Introductionmentioning

confidence: 99%

Pavement Crack Extraction Method from Mobile Laser Scanning Point Cloud

Phan

Huynh

2022

Advances in Civil Engineering

View full text Add to dashboard Cite

The automatic detection of cracks in the road surface is of great significance for maintaining the road surface to ensure the safety of moving vehicles. The Hokuyo UTM 30 LX 2D laser scanner in this study is used to observe the road surface containing two cracks and distress areas. As a result, dense point clouds are created. The road surface is automatically extracted from the point clouds based on the geometry of the two curb lines. The normal vector of the points is calculated based on the principal component analysis method. Points belonging to cracks and zones of distress are extracted from the intensity gradient and the inclination angle between the two normal vectors of the neighboring points and then converted to binary images. The crack edges are extracted based on the Sobel operator. Although salt-and-pepper spots due to crack points extraction using intensity gradient affect the definition of crack edges, especially small cracks, large cracks and distress areas are extracted clearly. Research results show that reflectance intensity and elevation variation combination lead to the efficiency of crack extraction and distress area.

show abstract

An Automatic Image Processing Algorithm Based on Crack Pixel Density for Pavement Crack Detection and Classification

Cited by 52 publications

References 49 publications

A Pavement Crack Detection Method Based on Multiscale Attention and HFS

A Pavement Crack Detection Method Based on Multiscale Attention and HFS

AugMoCrack: Augmented morphological attention network for weakly supervised crack detection

Pavement Crack Extraction Method from Mobile Laser Scanning Point Cloud

Contact Info

Product

Resources

About