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• Propose a computer vision method for detecting asphalt pavement segregation. • Employ image texture analysis for characterizing pavement surface condition. • Extreme gradient boosting machine and deep neural network are used for classification. • Attractive repulsive center-symmetric local binary pattern is used for texture computation. • XGBoost has achieved the best detection accuracy with accuracy rate = 0.95. Aggregate segregation is a major form of defect that accelerates the pavement deterioration rate. Therefore, asphalt pavement segregation needs to be detected accurately and early during the quality survey process. This study proposes and verifies a computer vision based method for automatic identification of aggregate segregation. The new method includes Extreme Gradient Boosting Machine integrated with Attractive Repulsive Center-Symmetric Local Binary Pattern (ARCSLBP-XGBoost) and Deep Convolutional Neural Network (DCNN). Experimental results obtained from a repetitive random data sampling process with 20 runs show that the ARCSLBP-XGBoost is a capable approach for detecting asphalt pavement segregation with outstanding performance measurement metrics (classification accuracy rate = 0.95, precision = 0.93, recall = 0.98, and F1 score = 0.95). [ABSTRACT FROM AUTHOR]

Finding patches is a crucial step in a pavement performance survey. The study develops a machine learning and image processing algorithm-established automatic method for identifying asphalt pavement patches. The GrayLevel Co-Occurrence Matrix and image texture-based features derived from color channel statistics are used as input parameters to describe the condition of the pavement. For feature extraction, image processing methods like the projective integral of images, steerable filters, and an improved image thresholding method were used. Support Vector Machine is employed for categorizing the differentiating patched regions from non-patch ones. The suggested combination of image texture analytical methods has been trained using an IA data set created from 400 image samples. The feature set, which combines the characteristics of cracked objects with those generated from the projective integral, can produce the desired result. To make the model's execution simpler, a patch recognition program was created and implemented in MATLAB. As a result, the recently created method has the potential to be an instrument for traffic management organizations through the assessment of pavement performance. The results of the experiments indicate that the newly developed method may achieve excellent accuracy prediction results with a classifier performance rate of about 96%. Such a method could enable transportation authorities to assess the state of asphalt pavement. [ABSTRACT FROM AUTHOR]

The pavement distress information is essential for evaluating the pavement. Automatic pavement inspection systems can be so beneficial in providing needed information on pavement conditions. The bleeding is one of the asphalt pavement distresses and directly affects the pavement skid resistance and road safety. This study aims to develop an image-based system for the comprehensive evaluation of pavement bleeding. This evaluation consists of three main parts: bleeding occurrence detection, bleeding region segmentation, and severity-based classification of bleeding regions. For implementing the proposed system, deep learning-based model and transfer learning method are used in the detection part, and wavelet transform is the main process in the segmentation part. Also, gray-level co-occurrence matrix, wavelet transform, and signal to noise ratio are used to extract image texture-based features from various levels of the bleeding severity (low, mid, and high). Then a decision tree has been made based on extracted features for severity-based classification. The proposed system can indicate the bleeding index based on distress density and severity. Results show good performance in detection, segmentation, and severity-based classification parts, on the average of performance indices with 98%, 89%, and 93%, respectively. Therefore, the proposed system provides an efficient method for comprehensive pavement bleeding evaluation. [ABSTRACT FROM AUTHOR]

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To accurately evaluate the skidding resistance of asphalt pavements, a texture imaging device was developed to realize the standardized acquisition of pavement images. Based on the gray-level co-occurrence matrix and multifractal theory of texture structure, the influence of segregation degree and gradation type on the texture properties of asphalt pavement was studied. Meanwhile, a comprehensive evaluation index of skidding resistance was proposed for asphalt pavement. Furthermore, the attenuation characteristics of the anti-skidding performance for asphalt mixture were explored, and the corresponding attenuation model of asphalt pavement was established. The results show that the segregation degree and gradation type significantly affected the texture parameters and anti-skidding performance of asphalt mixture. Specially, with an increase in the segregation degree of coarse aggregate, the parameters of energy, entropy, and multifractal spectrum width gradually increased, whereas the inertial moment gradually decreased. The variation range of the multifractal spectrum difference initially increased and subsequently decreased. For the texture parameters such as energy, entropy, inertial moment, and multifractal spectrum width Δα, the values of the asphalt mixture with larger nominal maximum particle were higher than those of the mixture with smaller nominal maximum particle, whereas the multifractal spectrum difference value showed the opposite law. In addition, the texture parameters of energy, entropy, and multifractal spectrum width exhibited good linear correlation with the texture depth (TD) of asphalt mixtures with various segregation levels and gradation types. The index based on the texture parameters of energy, entropy, and multifractal spectrum width effectively evaluated the skidding resistance of asphalt pavements, which showed the same trend as the TD with the increase of the abrasion number. The achievement provides an effective solution for the evaluation of skidding resistance and attenuation characteristics of asphalt mixtures. [ABSTRACT FROM AUTHOR]

During the phase of periodic asphalt pavement survey, patched and unpatched potholes need to be accurately detected. This study proposes and verifies a computer vision-based approach for automatically distinguishing patched and unpatched potholes. Using two-dimensional images, patched and unpatched potholes may have similar shapes. Therefore, this study relies on image texture descriptors to delineate these two objects of interest. The texture descriptors of statistical measurement of color channels, the gray-level cooccurrence matrix, and the local ternary pattern are used to extract texture information from image samples of asphalt pavement roads. To construct a classification model based on the extracted texture-based dataset, this study proposes and validates an integration of the Support Vector Machine Classification (SVC) and the Forensic-Based Investigation (FBI) metaheuristic. The SVC is used to generalize a classification boundary that separates the input data into two class labels of patched and unpatched potholes. To optimize the SVC performance, the FBI algorithm is utilized to fine-tune the SVC hyperparameters. To establish the hybrid FBI-SVC framework, an image dataset consisting of 600 samples has been collected. The experiment supported by the Wilcoxon signed-rank test demonstrates that the proposed computer vision is highly suitable for the task of interest with a classification accuracy rate = 94.833%. [ABSTRACT FROM AUTHOR]

• Pavement raveling detection based on image processing is proposed. • Texture analysis techniques are employed for feature extraction. • Two scenarios of GLCM and LBP-GLCM are utilized in the feature extraction stage. • XGBoost classifier is employed for the classification of raveling and no-raveling images. • The results of LBP-GLCM feature extraction achieved better performance for raveling detection. The raveling of asphalt pavement is the primary cause of decreasing road safety, comfort, and service life. Because of the asphalt's complex texture, automatic raveling detection from image samples is a challenging operation. In this study, a computer vision technique, based on image texture features, for automatic detection of asphalt pavement raveling is proposed and verified. Two scenarios are taken into account for feature extraction. First, texture features from images are extracted using the traditional GLCM (Gray-Level Co-occurrence Matrix) algorithm. Second, the images are subjected to LBP (Local Binary Pattern) and then GLCM is employed to extract texture features. Utilizing the eXtreme Gradient Boost (XGBoost) technique, two models are built using the mentioned feature extraction scenarios and then compared. The results indicate that compared to the first scenarios prediction performance (Accuracy, Precision, Recall, and F1-Score are all approximately equal to %81), the second feature extraction scenario can offer higher prediction performance (Accuracy, Precision, Recall, and F1-Score are all approximately equal to %97). In order to demonstrate the model's generalizability, a separate dataset is tested. Due to the acceptable performance values for this dataset (with more than %97 in terms of Accuracy, Precision, Recall, and F1-Score), the suggested model can be beneficial for transportation agencies to enhance the efficiency of road inspection activities. [ABSTRACT FROM AUTHOR]

Crack disease is one of the most serious and common diseases in road detection. Traditional manual methods for measuring crack detection can no longer meet the needs of road crack detection. In previous work, the authors proposed a crack detection method for asphalt pavements based on an improved YOLOv5s model, which is a better model for detecting various types of cracks in asphalt pavements. However, most of the current research on automatic pavement crack detection is still focused on crack identification and location stages, which contributes little to practical engineering applications. Based on the shortcomings of the above work, and in order to improve its contribution to practical engineering applications, this paper proposes a method for segmenting and analyzing asphalt pavement cracks and identifying parameters based on image processing. The first step is to extract the crack profile through image grayscale, histogram equalization, segmented linear transformation, median filtering, Sauvola binarization, and the connected domain threshold method. Then, the magnification between the pixel area and the actual area of the calibration object is calculated. The second step is to extract the skeleton from the crack profile images of asphalt pavement using the Zhang–Suen thinning algorithm, followed by removing the burrs of the crack skeleton image using the connected domain threshold method. The final step is to calculate physical parameters, such as the actual area, width, segments, and length of the crack with images obtained from the crack profile and skeleton. The results show that (1) the method of local thresholding and connected domain thresholding can completely filter noise regions under the premise of retaining detailed crack region information. (2) The Zhang–Suen iterative refinement algorithm is faster in extracting the crack skeleton of asphalt pavement, retaining the foreground features of the image better, while the connected-domain thresholding method is able to eliminate the missed isolated noise. (3) In comparison to the manual calibration method, the crack parameter calculation method proposed in this paper can better complete the calculation of crack length, width, and area within an allowable margin of error. On the basis of this research, a windowing system for asphalt pavement crack detection, WSPCD1.0, was developed. It integrates the research results from this paper, facilitating automated detection and parameter output for asphalt pavement cracks. [ABSTRACT FROM AUTHOR]

Nonuniform or segregated asphalt mixture has been critical in asphalt pavement construction, accelerating pavement damage and reducing pavement service life. Existing uniformity quantification methods have not considered the aggregate skeleton structure that impacts the mixture loading and durability. Under the isotropic hypothesis, this paper proposed a nondestructive, real-time, and rapid approach to measuring asphalt mixture uniformity using image processing technology (IPT). A total of 1,188 images were first collected from a new construction site and then preprocessed to identify the coarse aggregate. The aggregate particle and skeleton structure factors were obtained, including particle quantity and position distribution, particle axial length ratio, particle orientation, and distance between particles. We used the entropy weight method to combine these four factors to propose a uniformity quantification index (UQI). Finally, UQI was compared with two existing uniformity quantification indices in evaluation consistency. The results show that the uniformity level ranked by the UQI was more consistent with the visual observation than the other two indices. Among the four factors, the particle quantity and position distribution, particle axial length ratio, and distance between particles correlate well with the asphalt mixture uniformity. Moreover, for the considered asphalt mixtures, stable quantification results need a minimum image size of 250 by 556 mm. Even smaller image sizes produced misleading uniformity quantification results. [ABSTRACT FROM AUTHOR]

Simultaneous pixel‐level detection of multiple distresses and surface design features on complex asphalt pavements is a critical challenge in intelligent pavement survey. This paper proposes a deep‐learning model named ShuttleNet to provide an efficient solution for this challenge by implementing robust semantic segmentation on asphalt pavements. The proposed ShuttleNet aims at repeating the encoding–decoding round freely or even endlessly such that the contexts at different resolution levels can be learned and integrated many times for enhanced latent representations. Additionally, a new and efficient connection method called memory connection is also proposed in the paper and deployed in the ShuttleNet model to provide shortcut connections between successive encoding–decoding rounds. The proposed memory connection can partially or entirely carry the decoded information at different resolution levels into the next encoding–decoding round. Pairing 3D pavement images with 2D pavement images, the proposed ShuttleNet model is applied to detect multiple distresses and surface design features on asphalt pavements simultaneously, including pavement cracks, potholes, sealed cracks, patches, markings, expansion joints, and the pavement background. Experimental results demonstrate that the mean F‐measure and mean intersection‐over‐union attained by the recommended architectural variation of the proposed ShuttleNet model on 1500 testing image pairs are 92.54% and 0.8657 respectively. According to the performance comparisons using both private and public datasets, the proposed ShuttleNet model can yield a noticeably higher detection accuracy, compared with four state‐of‐the‐art models for semantic segmentation. [ABSTRACT FROM AUTHOR]

In order to improve the prediction accuracy regarding low compaction level of asphalt pavement, this paper carries out indoor tests to detect the voids and dielectric constants of AC-13, AC-16 and AC-25 asphalt mixtures, obtaining their relationship equations via linear fitting and determining the dielectric constant judgment threshold of low compaction level segregation risk points ε 1 . Based on the common mid-point method, three-dimensional ground-penetrating radar is used to obtain the dielectric constant of the physical engineering test section. The researcher can draw the distribution map of the low compaction level segregation risk area according to the judgment threshold ε 1 of the rough segregation risk points; divide the connected risk areas; determine the regional convex hull; and calculate the regional indicators such as the regional area, the ratio of the convex risk points and the mean value of the regional dielectric constant. The response surface analysis method is used to acquire the model of risk area index and core void ratio. The model is employed to predict and verify the core void ratio in the risk area of the road section and verify the accuracy of the model. The results show that the error range between the predicted voids and the measured voids is −0.4%~+0.4%, and the mean absolute value of the error is 0.25%. Compared with the mean measured voids of 6.63%, the relative error is 3.77%, indicating that the model can accurately predict the regional low compaction level segregation degree. [ABSTRACT FROM AUTHOR]

• Asphalt mixture segregation detection using image texture features. • Otsu algorithm combined with Canny edge detection to obtain aggregate distribution image. • The two-dimensional entropy is used to calculate aggregate distribution uniformity. • A new indicator "U" is proposed to indicate the degree of asphalt mixture segregation. Surface segregation in asphalt mixture caused by the homogeneity of aggregates seriously influences the service life and road performance of asphalt pavements. However, existing segregation detection procedures are cumbersome and the detecting results are heavily influenced by the moisture content of the surface asphalt mixture. Therefore, this paper innovatively proposes two-dimensional (2D) entropy of the chunking image in calculating uniformity complexity (U) to evaluate surface segregation of bituminous mixtures with digital image processing (DIP). Before image chunking, Otsu algorithm and Canny edge detection are properly combined to obtain a more accurate aggregate distribution image. Furthermore, the feasibility of this detecting method is verified through a simulation experiment, a grading comparison experiment, and a noise interference experiment. These experimental results demonstrated that this study presents an efficient approach for the segregation detection of asphalt mixture with high accuracy. [ABSTRACT FROM AUTHOR]

Traffic noise is the perpetual form of environmental pollution adversely affecting human health in urban environments. This literature review, intended for pavement researchers and professionals, looks at the continuously evolving low-noise asphalt pavement technologies and the techniques which can be used to evaluate them. Test methods for determining the acoustical properties of asphalt pavements are reviewed, in both the laboratory and field environments. The Close-Proximity (CPX) method is the most commonly used field test for pavement acoustics, followed by the Statistical Pass-By (SPB) and On-Board Sound Intensity (OBSI) methods. SPB seems is the most comprehensive methods, while the CPX is more practical. Methods for measuring the acoustical properties in the laboratory include the impedance tube for sound absorption and laboratory pavement noise simulators; with only the larger drum methods being able to produce conditions similar to in-situ. Methods for noise-relevant non-acoustical characteristics like surface texture, porosity and airflow resistivity were also reviewed. Optimizing surface texture at the macro-scale was found to be important in reducing tire/road noise. For pavement types, porous asphalt concrete (PAC) and its variants result in low-noise properties the most reliably, while having some drawbacks in durability and maintenance. Finally, various acoustical performance prediction models were discussed. [ABSTRACT FROM AUTHOR]

The micro-texture of the aggregates of a pavement layer has a direct influence on their resistance. Whatever the position of these aggregates in a pavement structure, they must withstand, during construction or during life, the stresses of attrition and impact. In this study, a series of mechanical tests (Proctor, Los-Angeles and Micro-Deval) are carried out on grains of local materials (limestone and shale), the degree of crushing of the grains has been quantified using the concept of fractal dimension. The fractal dimension was calculated for the different grains constituting the samples before and after each test, with the use of two two-dimensional 2D methods (Masses Method at the scale of a sample and the Box Counting Method at the scale of a grain) and a three-dimensional 3D method (Blanket on a grain scale) which is based on the use of the difference between erosion and dilation. We seek to determine from these methods the correlation between the two fractal dimensions, namely 2D and 3D and study the influence of different parameters on the mechanical characteristics of the materials chosen: the shape and size of the grains, the presence or absence of water, the stress intensity as well as the nature of the material. The results obtained show that the three-dimensional method has a positive effect on the description of the 3D microstructure of the surface of the grains subjected to the various mechanical tests. [ABSTRACT FROM AUTHOR]

Pavement rutting refers to surface depression in the wheel-path along an asphalt road which causes loss of steering control and consequently leads to serious traffic accidents. Hence, it is necessary to develop powerful methods to accurately recognize pavement rutting during road condition survey. This study presents a novel computer vision-based model to automatically identify rutting on asphalt pavement road. The model is established based on a hybridization of image processing techniques (ITPs), least squares support vector classification (LSSVC), dynamic feature selection (FS) method, and forensic-based investigation (FBI). The ITPs, including Gabor filter and discrete cosine transform were employed to implement texture computation for image data. These techniques are used to generate an initial set of extracted features describing rutting and non-rutting states. The extracted features were then refined by a wrapper-based feature selection (FS) method to determine set of highly relevant features. LSSVC models were used to learn the categorization of rutting and non-rutting based on the refined features and hyper-parameters optimized by the FBI metaheuristic. The final LSSVC prediction model with the most desired prediction accuracy can be obtained once the process of the FBI's optimization terminates. A dataset of 2000 image samples has been collected during field trip of pavement survey in Da Nang city (Vietnam) to construct and evaluate the newly developed model. The statistical results obtained from a k-fold cross-validation have demonstrated that the hybrid FBI-LSSVC-FS model can achieve the most desired rutting recognition performance with accuracy rate, precision, recall, and F1 score of 98.9%, 0.994, 0.984 and 0.989, respectively. Therefore, this paper contributes to the body of knowledge by proposing a novel AI-based prediction model to assist transportation agencies in the task of periodic asphalt pavement survey. [ABSTRACT FROM AUTHOR]