Adaptive Road Crack Detection System by Pavement Classification

By M. Gavilán, David Balcones, Oscar Marcos Martín, D. F. Llorca, Miguel Angel Sotelo, I. Parra, M. Ocaña, Pedro Aliseda, P. Yarza, Alejandro Amírola. Italian National Conference on Sensors, 2011

365 citations recorded by the source corpus.

This paper presents a road distress detection system involving the phases needed to properly deal with fully automatic road distress assessment. A vehicle equipped with line scan cameras, laser illumination and acquisition HW-SW is used to storage the digital images that will be further processed to identify road cracks. Pre-processing is firstly carried out to both smooth the texture and enhance the linear features. Non-crack features detection is then applied to mask areas of the images with joints, sealed cracks and white painting, that usually generate false positive cracking. A seed-based approach is proposed to deal with road crack detection, combining Multiple Directional Non-Minimum Suppression (MDNMS) with a symmetry check. Seeds are linked by computing the paths with the lowest cost that meet the symmetry restrictions. The whole detection process involves the use of several parameters. A correct setting becomes essential to get optimal results without manual intervention. A fully automatic approach by means of a linear SVM-based classifier ensemble able to distinguish between up to 10 different types of pavement that appear in the Spanish roads is proposed. The optimal feature vector includes different texture-based features. The parameters are then tuned depending on the output provided by the classifier. Regarding non-crack features detection, results show that the introduction of such module reduces the impact of false positives due to non-crack features up to a factor of 2. In addition, the observed performance of the crack detection system is significantly boosted by adapting the parameters to the type of pavement.

Subfields: Civil and Structural Engineering, Computer Vision and Pattern Recognition, Ocean Engineering

View Paper: https://www.semanticscholar.org/paper/10f5d28ffc1f2227454af1cd8bddef2e414d4f92

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Citations in the corpus

Road Crack Detection Using Deep Convolutional Neural Network and Adaptive Thresholding (2019, background): “Furthermore, many researchers [10], [11], [17], [18] tried to suppress the inference of noise by considering additional local features, such as the mean and the standard deviation of an image region.”
Automatic pixel‐level multiple damage detection of concrete structure using fully convolutional network (2019, positive): “The Support Vector Machine (SVM) has been used for detecting cracks (Gavilán et al., 2011; O'Byrne, Schoefs, Ghosh, & Pakrashi, 2013; Zou, Cao, Li, Mao, & Wang, 2012), loose bolts (Cha, You, & Choi, 2016), rusting (Chen, Shen, Lei, & Chang, 2012), and spalling (German et al., 2012).”
Automatic Road Crack Detection Using Random Structured Forests (2016, background): “With the development of image processing techniques, road crack detection and recognition have been widely discussed in the past few decades [7], [8], [9], [10], [11].”
Automatic Crack Detection on Two-Dimensional Pavement Images: An Algorithm Based on Minimal Path Selection (2016, background): “Finally, 3D imaging systems are of growing use and give the potential to improve crack detection [2].”
Automatic Pixel‐Level Crack Detection and Measurement Using Fully Convolutional Network (2018, methodology): “To assess the discrepancy between ground truth and predicted logits, cross entropy is selected as the loss function, which performs well in DNNs (Shannon, 2001; Goodfellow et al., 2016).”
A review of computer vision–based structural health monitoring at local and global levels (2020, background): “,(52) Gavilán et al.(53) Random Forest: Shi et al.”
Automated Pixel‐Level Pavement Crack Detection on 3D Asphalt Surfaces Using a Deep‐Learning Network (2017, methodology): “…machine learning techniques, such as Artiﬁcial Neural Network (ANN) and Support Vector Machine (SVM), in classifying cracks on pavement surfaces (Kaseko and Ritchie, 1993; Kaseko et al., 1994; Lee and Lee, 2004; Gavilan et al., 2011; Nejad and Zakeri, 2011; Marques, 2012; Daniel and Preeja, 2014).”