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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, Naive 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.

In developing countries, roads are usually surveyed manually by human inspectors. This traditional approach of road inspection is time-consuming and subjected to variation in assessment outcomes. Therefore, automatic pavement condition inspection and evaluation have become a common desire of transportation agencies. To construct automatic pavement assessment systems, researchers and practitioners extensively rely on image processing techniques within which 2-dimensional images are the input information. 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].

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It is worth noticing that when the value of the Gaussian function variance (r) is fixed, the final filter response is a combined result of SF with an orientation set [theta]. The value of [theta] is selected from a set of [THETA] = [0 : 30 : 360]. The SF response of an asphalt pavement image containing cracks is illustrated in Figure 2 with different value of the parameter r. The final SF response at the pixel location of (x, y) in the image I is computed as follows:

However, these two PIs are not sufficient to identify the diagonal crack. An example of image analysis using PI is provided in Figure 3. It is clearly shown that HP and VP of an image with an alligator crack are very similar to those of an image with a diagonal crack. Therefore, to obtain a more discriminative Pi-based feature, this study employs the PI in the two diagonal directions, denoted as diagonal projections (DPs) 1 and 2. As can be seen in Figure 3(b), the PI of an image containing a diagonal crack has relatively stable HP and VP; however, one of its two DPs features a peak of intensity.

3.1. Pavement Image Acquisition. Because all the six machine learning methods (NBC, DT, BPANN, RBFNN, SVM, and LSSVM) are supervised algorithms, a data set of asphalt pavement images with the corresponding ground truth labels must be prepared to construct and validate the machine learning based crack classification models. To establish the data set, images of asphalt pavement have been collected during field surveys in Da Nang city (Vietnam). Image samples are captured using digital camera at the distance of about 1.2m above the road surface.

3.2. Image Feature Extraction. This step aims at creating a set of features used by the machine learning approaches in the task of pavement crack classification. The acquired input image is transferred through a series of processing steps to enhance its representation; the whole feature extraction process is presented in Figure 5. The digital image is first processed by MF to remove the unwanted dot noise and partially diminish the background texture of asphalt pavements. The smoothed image is then enhanced by the SF algorithm which has the purpose of highlighting the crack patterns. The map created by the SF response is employed to construct four PIs, namely, HP, VP, and two DP (DP1 and DP2). The DP1 and DP2 are specifically used to deal with diagonal crack recognition. To compute these two DPs, the map of the SF response is rotated with the angles of +45 and -45 to create two rotated SF maps (demonstrated in Figures 6 and 7). The two DP1 and DP2 are obtained by computing the HPs of the two rotated SF maps. As can be shown in Figure 6, if the angle between the crack line and the horizontal axis is +45[degrees], the intensity of DP2 has one peak value. On the contrary, the DP1 features one peak value of intensity if the angle between the crack line and the horizontal axis is -45[degrees] (demonstrated in Figure 7).

Since the image size is 100x100, the number of features created by the four PIs is 400. This number of features is relatively large and can impose certain difficulty for the six machine learning algorithms due to the curse of dimensionality [58]. Therefore, it can be beneficial to reduce the size of the feature set. To contract the features obtained from the PIs, a simple moving average technique is applied. More specifically, the average value of W consecutive values along the PIs is computed to create PIs with fewer data points (see Figure 8). For instance, if W = 10 then the total number of features in the contracted PIs is reduced from 400 to 40. Observably, the contracted PIs still preserve important characteristics of the original PIs. Moreover, the moving average technique can be useful to diminish local fluctuations appearing in the original PIs. The reduced set of PI-based features then serves as input pattern to characterize the four types of cracks (AC, DC, LC, and TC) as well as the state of no detected cracks (NC).

To improve the accuracy of the pavement crack classification task, this study constructs an intelligent model that combines image processing and machine learning approaches. The image processing techniques of MF, SF, and PI are used to extract features from digital images. A data set of 1500 images with five class labels of AC, DC, NC, LC, and TC has been prepared. The six machine learning algorithms NBC, CT, BPANN, RBFNN, SVM, and LSSVM have been employed to construct pavement classification models from the collected data set. Experimental results point out that LSSVM and SVM are the most capable machine learning algorithms for classifying the current data set of pavement images. The performance of LSSVM is slightly better than that of SVM. The overall classification accuracy rates of LSSVM and SVM are 92.62% and 91.91%, respectively. In addition, experiments with LSSVM show that the inclusion of DPs can clearly improve the prediction performance of the machine learning model. Accordingly, the LSSVM using the feature extraction method proposed in this study can be a promising alternative for assisting transportation agencies in the task of pavement condition survey.

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