Automatic Pavement Crack Detection Using Texture and Shape Descriptors

Hu, Yinlong; Zhao, Chenglin; Wang, Hongnan

doi:10.4103/0256-4602.62225

Cited by 78 publications

(35 citation statements)

References 10 publications

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“…With the development of machine learning, methods based on machine learning have become an important branch of pavement crack detection. Hu et al [27] designed the pavement as a textured surface, and then used a support vector machine (SVM) for classification. Cord et al [28] used AdaBoost to detect defects based on patterned text information, and the image of the pavement surface was distinguished from the pavement surface.…”

Section: B Machine Learningmentioning

confidence: 99%

APLCNet: Automatic Pixel-Level Crack Detection Network Based on Instance Segmentation

Zhang

Chen

Wang³

et al. 2020

IEEE Access

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The accurate and automatic detection of pavement cracks is essential for pavement maintenance. However, automatic crack detection remains a challenging problem due to the inconspicuous visual features of cracks in complex pavement backgrounds, the complicated shapes and structures of cracks, and the influences of weather changes and noise. In recent years, with the development of artificial intelligence technology, crack detection methods based on classification and semantic segmentation have laid a good foundation for the automation of pavement crack detection. However, there remain shortcomings in the comprehensive acquisition of pavement crack attribute information and detection accuracy. To solve these problems, this paper proposes an instance segmentation network for pavement crack detection. The network can simultaneously obtain the crack category, position, and mask, and can realize end-to-end pixel-level crack detection. A semantic segmentation branch is first added to Mask R-CNN. This branch can extract the bottom-level detail information of the cracks and ultimately improves the accuracy of crack mask prediction. An adaptive feature fusion module is then designed. During feature fusion, this module highlights the attribute information and location information of cracks according to the channel attention mechanism and the spatial attention mechanism. Finally, these two modules are integrated to form an automatic pixel-level crack detection network, namely APLCNet. Without any embellishment, APLCNet achieves a precision of 92.21%, a recall of 94.89%, and an F1-score of 93.53% on the challenging public CFD dataset, thereby outperforming CrackForest and MFCD for pixel-wise crack detection. Moreover, APLCNet achieves a 16.5% mask AP on the self-captured GDPH dataset, thereby surpassing Mask R-CNN and PANet.

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Section: B Machine Learningmentioning

confidence: 99%

APLCNet: Automatic Pixel-Level Crack Detection Network Based on Instance Segmentation

Zhang

Chen

Wang³

et al. 2020

IEEE Access

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“…Results are better than with the global thresholding in terms of both sensitivity and specificity. [25] suggests an approach able to cope with the complex background texture, uneven illumination and nonuniform background of roads. Cracks are seen as inhomogeneities in the background texture.…”

Section: Related Workmentioning

confidence: 99%

“…Then, region features are the average intensity, the area and the standard deviation of shape distance histogram, and the ELM supervised classifier is used. In [25], small or block-like connected components are eliminated, and components with same orientation and space adjacently are merged together. Only real linear cracks remain.…”

Section: Related Workmentioning

confidence: 99%

Automatic crack detection on concrete floor images

Simler

Trostmann

Berndt

2019

Photonics and Education in Measurement Science 2019

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This paper presents an algorithm detecting automatically a wide variety of cracks on monochrome images of concrete floors. It is part of a vision system, generating a crack map to support the condition monitoring for buildings. The suggested method uses successively radiometric, geometric and contextual information. An automatic supervised adaptive intensity-threshold method handles radiometry information. A threshold-based method was chosen because it can separate even thin cracks from the background. In order to cope with different image cluster intensities, first a clustering algorithm separates regions of different intensities, and second the threshold is adaptive at the cluster level; being a continuous function of the cluster intensity. At some points, function values were learned via supervised classification. Then, we performed an interpolation between these points in order to get a threshold whatever the cluster intensity (continuity). At this step, we have a thresholded image. However, due to texture, non-crack dark defects and dirtiness we have false positives. In order to overcome this problem, connected pixels are grouped into regions, and after discarding small regions, a size-dependent geometrical shape filter is suggested. The shape of a crack region depends on its area, and this relation was empirically learned. A region is retained if its shape features are above some area depending thresholds. However, mainly due to texture, we have still false positives because some non-crack entities have radiometry and geometry of cracks. Fortunately, they are often small isolated regions and are discarded via an isolation filter. Tests performed on many images show very encouraging results.

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“…An SVM is a supervised computational learning model with an associated training algorithm that can be used, for a given set of input data, to assign a probability to two possible categories to which the set of input data may belong (Van Looy et al, 2007). Previous studies have employed SVMs in pavement engineering to, for example, estimate the pavement serviceability ratio and detect pavement cracking (Hu et al, 2010;Yan et al, 2011). The SVM training algorithm uses input training data to build a model that can assign probabilities to new input data sets.…”

Section: Computational Modelmentioning

confidence: 99%