Comparison of Supervised Classification Techniques for Vision-Based Pavement Crack Detection

Mokhtari, Soroush; Wu, Liuliu; Yun, Hae‐Bum

doi:10.3141/2595-13

Cited by 47 publications

(23 citation statements)

References 19 publications

(21 reference statements)

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“…Recently, the automatized acquisition and processing of spatial data on the distortions of roads and runways have been a focus in many studies [28][29][30][31][32][33][34]. In the studies, the authors focused mainly on the interpretation and analysis of road and runway records.…”

Section: Existing Information Models For Monitoring Distortion On Runmentioning

confidence: 99%

Improved Decision-Making Geo-Information System for Continuous Monitoring of Deformations on Airport Infrastructure

Doler

Kovačić

2018

IJGI

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Heavy burdens are placed on runways daily; thus, it is necessary to monitor these surfaces regularly. The appearance of distortions can cause difficulties for aircraft when they land or take off, which occurs mainly for lighter planes. This can be seen in reduced grip, uneven use of tires, problems with measurement systems, and unbalanced drive, which can result in damage to or even destruction of the plane or its parts. For that purpose, the model of continuous monitoring of distortions on and under the surface of the runway was created. This model is based on geodetic and geophysical methods. The method for monitoring distortions was developed into a decision-making model in the shape of a geo-information base. The results provide an image of the state of the runway including damage on and under the surface, which are the most common causes of runway distortions. The data are automatically recorded in the newly established information system, which supports the decision making regarding renovations and runway maintenance. The measurement on the runway takes 30 min, which does not lead to a long closure of the airport and, thus, reduces traffic jams and additional costs.

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Section: Existing Information Models For Monitoring Distortion On Runmentioning

confidence: 99%

Improved Decision-Making Geo-Information System for Continuous Monitoring of Deformations on Airport Infrastructure

Doler

Kovačić

2018

IJGI

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“…Ouma and Hahn [4] constructed an automatic recognition approach of linear cracks based on the wavelet-morphology and circular Radon transform methods. Mokhtari et al [13] utilized artificial neural network (ANN), decision tree, and k-nearest neighbors to classify pavement images into "Crack" and "No Crack" labels; the ANN was proved to be superior to the decision tree and knearest neighbors. Tiled fuzzy Hough transform was applied to detect near straight segments of cracks embedded in pavement textures [14]; this study confirms that the fuzzy Hough transform is effective in diminishing the contribution of texture and noise pixels.…”

Section: Mathematical Problems In Engineeringmentioning

confidence: 99%

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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“…Rababaah [26] carried out a comparative work which investigated the performance of multilayer perceptron neural network, genetic algorithms, and self-organizing maps in pavement crack classification. Mokhtari et al [27] recently employed neural network models to tackle the problem of interest; this study concluded that neural network models are more capable than other learning strategies of decision tree and knearest neighbor. Banharnsakun [28] combined the advantage of metaheuristic and neural network for pavement surface distress detection and classification; the metaheuristic of artificial bee colony was used in the phase of image segmentation, and the subsequent classification task was performed by neural network.…”

Section: Introductionmentioning

confidence: 97%

Fast Local Laplacian‐Based Steerable and Sobel Filters Integrated with Adaptive Boosting Classification Tree for Automatic Recognition of Asphalt Pavement Cracks

Hoang

Nguyen

2018

Advances in Civil Engineering

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Effective road maintenance requires adequate periodic surveys of asphalt pavement condition. The manual process of pavement assessment is labor intensive and time-consuming. This study proposes an alternative for automating the periodic surveys of pavement condition by means of image processing and machine learning. Advanced image processing techniques including fast local Laplacian filter, Sobel filter, steerable filter, and projection integral are employed for image enhancement and analysis to extract useful features from digital images. Based on the features produced by these image processing techniques, adaptive boosting classification tree is used to perform pavement crack recognition tasks. A dataset of image samples consisting of five classes (alligator crack, diagonal crack, longitudinal crack, noncrack, and transverse crack) has been collected to construct and verify the performance of the adaptive boosting classification tree. The experimental results show that the proposed approach has achieved a high crack classification accuracy which is roughly 90%. Therefore, the newly developed model is a promising alternative to help transportation agencies in pavement condition evaluation.

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Comparison of Supervised Classification Techniques for Vision-Based Pavement Crack Detection

Cited by 47 publications

References 19 publications

Improved Decision-Making Geo-Information System for Continuous Monitoring of Deformations on Airport Infrastructure

Improved Decision-Making Geo-Information System for Continuous Monitoring of Deformations on Airport Infrastructure

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

Fast Local Laplacian‐Based Steerable and Sobel Filters Integrated with Adaptive Boosting Classification Tree for Automatic Recognition of Asphalt Pavement Cracks

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