A novel method for asphalt pavement crack classification based on image processing and machine learning

Hoang, Nhat‐Duc; Nguyen, Quoc-Lam

doi:10.1007/s00366-018-0611-9

Cited by 154 publications

(97 citation statements)

References 42 publications

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“…As demonstrated in the previous works of Cubero-Fernandez et al [6] and Hoang and Nguyen [2], this image enhancement technique is particularly useful for differentiating the crack patterns and the background texture of asphalt pavement. In addition to crack detection, SF has been successfully employed in other tasks of the computer vision field such as object tracking, text classification, and distress recognition [3,[24][25][26].…”

Section: Steerable Filter (Sf)mentioning

confidence: 96%

“…Nevertheless, automatic crack detection and classification still face significant challenges including the complexity of the pavement texture, unexpected objects, nonuniform illumination, weak signals of crack patterns, the inhomogeneity of cracks, and the diversity of crack patterns [2,5,15]. Therefore, more studies should be dedicated to improving the effectiveness of pavement classification models.…”

Section: Mathematical Problems In Engineeringmentioning

confidence: 99%

“…Hoang and Nguyen [2] employed the image processing methods of Steerable Filters and Projective Integral for the feature extraction task as well as machine learning for classification task. Although the machine learning based method proposed in Hoang and Nguyen [2] has a good performance, this method cannot effectively recognize diagonal crack patterns.…”

Section: Mathematical Problems In Engineeringmentioning

confidence: 99%

“…Accurate and timely recognition of pavement distress as well as pavement health monitoring measures has increasingly become an integral part of the regional road maintenance system [1]. It is because early detection of pavement distress can help to develop costeffective rehabilitation methods and prevent the reduction in service life of pavement structures [2].…”

Section: Introductionmentioning

confidence: 99%

“…Various intelligent methods are then employed to enhance and transform the images to highlight the objects of interest which are pavement cracks [5]. Instead of analyzing the whole image, a set of useful features can be extracted from the image to detect the status of crack and to distinguish the type of cracks [2,6].…”

Section: Introductionmentioning

confidence: 99%

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Automatic Recognition of Asphalt Pavement Cracks Based on Image Processing and Machine Learning Approaches: A Comparative Study on Classifier Performance

Hoang

Nguyen

2018

Mathematical Problems in Engineering

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Periodic surveys of asphalt pavement condition are very crucial in road maintenance. This work carries out a comparative study on the performance of machine learning approaches used for automatic pavement crack recognition. Six machine learning approaches, Naïve Bayesian Classifier (NBC), Classification Tree (CT), Backpropagation Artificial Neural Network (BPANN), Radial Basis Function Neural Network (RBFNN), Support Vector Machine (SVM), and Least Squares Support Vector Machine (LSSVM), have been employed. Additionally, Median Filter (MF), Steerable Filter (SF), and Projective Integral (PI) have been used to extract useful features from pavement images. In the feature extraction phase, performance comparison shows that the input pattern including the diagonal PIs enhances the classification performance significantly by creating more informative features. A simple moving average method is also employed to reduce the size of the feature set with positive effects on the model classification performance. Experimental results point out that LSSVM has achieved the highest classification accuracy rate. Therefore, this machine learning algorithm used with the feature extraction process proposed in this study can be a very promising tool to assist transportation agencies in the task of pavement condition survey.

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Section: Steerable Filter (Sf)mentioning

confidence: 96%

Section: Mathematical Problems In Engineeringmentioning

confidence: 99%

Section: Mathematical Problems In Engineeringmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Automatic Recognition of Asphalt Pavement Cracks Based on Image Processing and Machine Learning Approaches: A Comparative Study on Classifier Performance

Hoang

Nguyen

2018

Mathematical Problems in Engineering

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Pixelwise asphalt concrete pavement crack detection via deep learning‐based semantic segmentation method

Huo

et al. 2022

Structural Contr & Hlth

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Explicit gaps exist between the advanced deep learning technologies and the less satisfied pixel-level crack detection algorithms. Therefore, this research sought to bridge this gap via outlining the deep neural network model for pixelwise pavement crack detection. Two state-of-the-art deep neural network models are constructed for the semantic segmentation of crack images. The first architecture, VGGCrackU-net, is composed of 10 3 Â 3 convolutional layers, 4 max-pooling layers, 4 up-sampling layers, and 4 concatenate operations. Another architecture, ResCrackU-net, is composed of 7-level residual units with a total of 22 convolutional layers. Asphalt concrete pavement crack images are collected by smartphones, action cameras, and automatic pavement monitoring systems from diverse functional classes of AC pavements. The crack images are manually labeled and double-checked by trained operators for quality insurance. After that, 500 crack images are randomly divided into training, validating, and test datasets according to the ratio of 3:1:1. Both architectures are trained on GPU facilitated Keras platform with Python version of 3.5, which demonstrated fast convergence. Results prove that the proposed models exhibit significant advantages for pixelwise crack detection when compared with the performance of widely used FCN net and PSPnet. Meanwhile, ResCrackU-net slightly outperforms VGGCrackU-net, which, however, can provide acceptable results as well. Though significant false negative and false positive errors are observed in both network models, the contributions are noticeable, which can provide innovative guidance for future work in figuring out solutions to the problems.

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Automatic bridge crack detection using boundary refinement based on real‐time segmentation network

Liu

Fang

et al. 2022

Structural Contr & Hlth

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Summary The appearance of surface cracks is an important feature of the early safety of the architecture being threatened, which is of great significance to maintaining the safety of the bridge. Bridge safety maintenance is still a challenging issue owing to noise interference and unclear bridge images. The detection method based on deep learning has achieved remarkable result in semantic segmentation, target detection, and other fields, but still be unable to focus on balancing speed and accuracy. Wherefore, we designed a new crack detection network‐dense boundary refinement network (DBR‐Net), which combines the advantages of STDC‐Net (Short‐Term Dense Concatenate network) and refinement network. STDC‐Net mainly improves the detection rate by eliminating redundant structures and optimizes the detailed information by binary cross‐entropy loss and dice loss. Finally, the detail aggregation module combines the shallow spatial details with the deep semantic information to predict the segmentation results. Among them, the refinement network refines the segmentation results of the offset map generated by the training stage and the test stage. We verified the feasibility of DBR‐Net on three datasets. At the same time, the detection accuracy rate on our self‐made crack dataset reached 97.54%. What's more worth mentioning is that our detection rate has reached 37.0 images per second (IPS), realizing real‐time crack detection.

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A novel method for asphalt pavement crack classification based on image processing and machine learning

Cited by 154 publications

References 42 publications

Automatic Recognition of Asphalt Pavement Cracks Based on Image Processing and Machine Learning Approaches: A Comparative Study on Classifier Performance

Automatic Recognition of Asphalt Pavement Cracks Based on Image Processing and Machine Learning Approaches: A Comparative Study on Classifier Performance

Pixelwise asphalt concrete pavement crack detection via deep learning‐based semantic segmentation method

Automatic bridge crack detection using boundary refinement based on real‐time segmentation network

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