Pavement Distress Detection Research Articles

A review on automated pavement distress detection methods

In recent years, extensive research has been conducted on pavement distress detection. A large part of these studies applied automated methods to capture different distresses. In this paper, a literature review on the distresses and related detection methods are presented. This review also includes commercial solutions. Thereafter, a gap analysis is conducted which is concluded that in particular the distresses related to pavement micro-texture need serious additional research in order to be implemented in a cost-effective fashion. Depth-related distresses are detectible fairly well, but rely on expensive tools.

IMPACT OF IMAGE RESOLUTION ON PAVEMENT DISTRESS DETECTION USING PICUCHA METHODOLOGY

An accurate and regular survey of the road surface distresses is a key factor for pavementrehabilitation design and management, allowing public managers to maximize the value of thecontinuously limited budgets for road improvements and maintenance. Manual pavement distresssurveys are labor-intensive, expensive and unsafe for highly-trafficked highways. Over the years,automated surveys using various hardware devices have been developed and improved forpavement field data collection to solve the problems associated with manual surveys. However,the reliable distress detection software and the data analysis remain challenging. This studyfocused on the analysis of a newly-developed pavement distress classification algorithm, called thePICture Unsupervised Classification with Human Analysis (PICUCHA) method, particularly theimpact of image resolutions on its classification accuracy. The results show that a non-linearrelationship exists between the classification accuracy and the image resolution, suggesting thatimages with a resolution around 1.24 mm/pixel may provide the optimal classification accuracywhen using the PICUCHA method. The findings of this study can help to improve more effectiveuses of the specialize software for pavement distress classification, to support decision makers tochoose cameras according to their budgets and desired survey accuracy, and to evaluate howexisting cameras will perform if used with PICUCHA.

Implementing textural features on GPUs for improved real-time pavement distress detection

The condition of municipal roads has deteriorated considerably in recent years, leading to large scale pavement distress such as cracks or potholes. In order to enable road maintenance, pavement distress should be timely detected. However, manual investigation, which is still the most widely applied approach toward pavement assessment, puts maintenance personnel at risk and is time-consuming. During the last decade, several efforts have been made to automatically assess the condition of the municipal roads without any human intervention. Vehicles are equipped with sensors and cameras in order to collect data related to pavement distress and record videos of the pavement surface. Yet, this data are usually not processed while driving, but instead it is recorded and later analyzed off-line. As a result, a vast amount of memory is required to store the data and the available memory may not be sufficient. To reduce the amount of saved data, the authors have previously proposed a graphics processing units (GPU)-enabled pavement distress detection approach based on the wavelet transform of pavement images. The GPU implementation enables pavement distress detection in real time. Although the method used in the approach provides very good results, the method can still be improved by incorporating pavement surface texture characteristics. This paper presents an implementation of textural features on GPUs for pavement distress detection. Textural features are based on gray-tone spatial dependencies in an image and characterize the image texture. To evaluate the computational efficiency of the GPU implementation, performance tests are carried out. The results show that the speedup achieved by implementing the textural features on the GPU is sufficient to enable real-time detection of pavement distress. In addition, classification results obtained by applying the approach on 16,601 pavement images are compared to the results without integrating textural features. There results demonstrate that an improvement of 27% is achieved by incorporating pavement surface texture characteristics.

GPU-Enabled Pavement Distress Image Classification in Real Time

Pavement assessment is a crucial process for the maintenance of municipal roads. However, the detection of pavement distress is usually performed either manually or offline, which is not only time-consuming and subjective, but also results in an enormous amount of data being stored persistently before processing. State-of-the-art pavement image processing methods executed on a CPU are not able to analyse pavement images in real time. To compensate this limitation of the methods, we propose an automated approach for pavement distress detection. In particular, GPU implementations of a noise removal, a background correction and a pavement distress detection method were developed. The median filter and the top-hat transform are used to remove noise and shadows in the images. The wavelet transform is applied in order to calculate a descriptor value for classification purposes. The approach was tested on 1549 images. The results show that real-time pre-processing and analysis are possible.

Framework of Data Acquisition and Integration for the Detection of Pavement Distress via Multiple Vehicles

AbstractStreet defects, such as potholes and sunken manholes, in general develop quickly compared to other pavement distresses, such as cracking and rutting. Those street defects can result in vehicle damage. This paper proposes an automated and innovative method to obtain up-to-date information about those street defects with the use of a mobile data collection kit mounted on vehicles. In each mobile data collection kit, a triaxial accelerometer and global positioning system sensor collect data for the detection of street defects. A local algorithm is embedded in the mobile data collection kit to increase the efficiency of a local data logging process and to perform a preliminary detection of street defects. At a back-end server, a more precise street defect detection algorithm enhances the performance of the proposed monitoring system by integrating data collected from multiple sensor-equipped vehicles. The street defect detection algorithm at the back-end server relies on a supervised machine learning ...

COMPARATIVE EVALUATION OF PAVEMENT CRACK DETECTION USING KERNEL-BASED TECHNIQUES IN ASPHALT ROAD SURFACES

With the increasing demand for the digital survey and acquisition of road pavement conditions, there is also the parallel growing need for the development of automated techniques for the analysis and evaluation of the actual road conditions. This is due in part to the resulting large volumes of road pavement data captured through digital surveys, and also to the requirements for rapid data processing and evaluations. In this study, the Canon 5D Mark II RGB camera with a resolution of 21 megapixels is used for the road pavement condition mapping. Even though many imaging and mapping sensors are available, the development of automated pavement distress detection, recognition and extraction systems for pavement condition is still a challenge. In order to detect and extract pavement cracks, a comparative evaluation of kernel-based segmentation methods comprising line filtering (LF), local binary pattern (LBP) and high-pass filtering (HPF) is carried out. While the LF and LBP methods are based on the principle of rotation-invariance for pattern matching, the HPF applies the same principle for filtering, but with a rotational invariant matrix. With respect to the processing speeds, HPF is fastest due to the fact that it is based on a single kernel, as compared to LF and LBP which are based on several kernels. Experiments with 20 sample images which contain linear, block and alligator cracks are carried out. On an average a completeness of distress extraction with values of 81.2%, 76.2% and 81.1% have been found for LF, HPF and LBP respectively.

COMPARATIVE EVALUATION OF PAVEMENT CRACK DETECTION USING KERNEL-BASED TECHNIQUES IN ASPHALT ROAD SURFACES

Abstract. With the increasing demand for the digital survey and acquisition of road pavement conditions, there is also the parallel growing need for the development of automated techniques for the analysis and evaluation of the actual road conditions. This is due in part to the resulting large volumes of road pavement data captured through digital surveys, and also to the requirements for rapid data processing and evaluations. In this study, the Canon 5D Mark II RGB camera with a resolution of 21 megapixels is used for the road pavement condition mapping. Even though many imaging and mapping sensors are available, the development of automated pavement distress detection, recognition and extraction systems for pavement condition is still a challenge. In order to detect and extract pavement cracks, a comparative evaluation of kernel-based segmentation methods comprising line filtering (LF), local binary pattern (LBP) and high-pass filtering (HPF) is carried out. While the LF and LBP methods are based on the principle of rotation-invariance for pattern matching, the HPF applies the same principle for filtering, but with a rotational invariant matrix. With respect to the processing speeds, HPF is fastest due to the fact that it is based on a single kernel, as compared to LF and LBP which are based on several kernels. Experiments with 20 sample images which contain linear, block and alligator cracks are carried out. On an average a completeness of distress extraction with values of 81.2%, 76.2% and 81.1% have been found for LF, HPF and LBP respectively.

Pavement distress detection and avoidance for intelligent vehicles

Pavement distresses and potholes represent road hazards that can cause accidents and damages to vehicles. The latter may vary from a simple flat tyre to serious failures of the suspension system, and in extreme cases to collisions with third-party vehicles and even endanger passengers' lives. The primary scientific aim of this study is to investigate the problem of road hazard detection for driving assistance purposes, towards the final goal of implementing such a technology on future intelligent vehicles. The proposed approach uses a depth sensor to generate an environment representation in terms of 3D point cloud that is then processed by a normal vector-based analysis and presented to the driver in the form of a traversability grid. Even small irregularities of the road surface can be successfully detected. This information can be used either to implement driver warning systems or to generate, using a cost-to-go planning method, optimal trajectories towards safe regions of the carriageway. The effectiveness of this approach is demonstrated on real road data acquired during an experimental campaign. Normal analysis and path generation are performed in post-analysis. This approach has been demonstrated to be promising and may help to drastically reduce fatal traffic casualties, as a high percentage of road accidents are related to pavement distress.

Hybrid ABC-ANN for pavement surface distress detection and classification

Pavement condition assessment plays an important role in the process of road maintenance and rehabilitation. However, the traditional road inspection procedure is mostly performed manually, which is labor-intensive and time-consuming. The development of automated detection and classification of distress on the pavement surface system is thus necessary. In this paper, a pavement surface distress detection and classification system using a hybrid between the artificial bee colony (ABC) algorithm and an artificial neural network (ANN), called “ABC-ANN”, is proposed. In the proposed method, first, after the pavement image is captured, it will be segmented into distressed and non-distressed regions based on a thresholding method. The optimal threshold value used for segmentation in this step will be obtained from the ABC algorithm. Next, the features, including the vertical distress measure, the horizontal distress measure, and the total number of distress pixels, are extracted from a distressed region and used to provide the input to the ANN. Finally, based on these input features, the ANN will be employed to classify an area of distress as a specific type of distress, which includes transversal crack, longitudinal crack, and pothole. The experimental results demonstrate that the proposed approach works well for pavement distress detection and can classify distress types in pavement images with reasonable accuracy. The accuracy obtained by the proposed ABC-ANN method achieves 20 % increase compared with existing algorithms.

A Review of Three-Dimensional Imaging Technologies for Pavement Distress Detection and Measurements

With the ever-increasing emphasis on maintaining road assets to a high standard, the need for fast accurate inspection for road distresses is becoming extremely important. Surface distresses on roads are essentially three dimensional (3-D) in nature. Automated visual surveys are the best option available. However, the imaging conditions, in terms of lighting, etc., are very random. For example, the challenge of measuring the volume of the pothole requires a large field of view with a reasonable spatial resolution, whereas microtexture evaluation requires very accurate imaging. Within the two extremes, there is a range of situations that require 3-D imaging. Three-dimensional imaging consists of a number of techniques such as interferometry and depth from focus. Out of these, laser imagers are mainly used for road surface distress inspection. Many other techniques are relatively unknown among the transportation community, and industrial products are rare. The main impetus for this paper is derived from the rarity of 3-D industrial imagers that employ alternative techniques for use in transportation. In addition, the need for this work is also highlighted by a lack of literature that evaluates the relative merits/demerits of various imaging methods for different distress measurement situations in relation to pavements. This overview will create awareness of available 3-D imaging methods in order to help make a fast initial technology selection and deployment. The review is expected to be helpful for researchers, practicing engineers, and decision makers in transportation engineering.

Improved Pavement Distress Detection Based on Contourlet Transform and Multi-Direction Morphological Structuring Elements

Traditional methods for crack distress detection cannot capture the geometric information of images and tend to amplify noise. In order to solve this problem, an improved algorithm based on contourlet transform and multi-direction morphological structuring elements is proposed. The new algorithm decomposed image into approximation coefficients and detail coefficients. Morphological erode operations is used to distinguish noise form detail information according to dependencies of contourlet coefficients, then nonlinear mapping function is used to modify the contourlet coefficients. And the enhanced image is obtained by contourlet inverse transform. Compared with other traditional methods, the experimental results indicate that our method can effectively extract the edges of cracks and evidently decrease the influence of noises. Moreover, it can provide good image processing speed.

FoSA: F* Seed-growing Approach for crack-line detection from pavement images

Most existing approaches for pavement crack line detection implicitly assume that pavement cracks in images are with high contrast and good continuity. This assumption does not hold in pavement distress detection practice, where pavement cracks are often blurry and discontinuous due to particle materials of road surface, crack degradation, and unreliable crack shadows. To this end, we propose in this paper FoSA — F* Seed-growing Approach for automatic crack-line detection, which extends the F* algorithm in two aspects. It exploits a seed-growing strategy to remove the requirement that the start and end points should be set in advance. Moreover, it narrows the global searching space to the interested local space to improve its efficiency. Empirical study demonstrates the correctness, completeness and efficiency of FoSA.

Pothole detection in asphalt pavement images

Pavement condition assessment is essential when developing road network maintenance programs. In practice, the data collection process is to a large extent automated. However, pavement distress detection (cracks, potholes, etc.) is mostly performed manually, which is labor-intensive and time-consuming. Existing methods either rely on complete 3D surface reconstruction, which comes along with high equipment and computation costs, or make use of acceleration data, which can only provide preliminary and rough condition surveys. In this paper we present a method for automated pothole detection in asphalt pavement images. In the proposed method an image is first segmented into defect and non-defect regions using histogram shape-based thresholding. Based on the geometric properties of a defect region the potential pothole shape is approximated utilizing morphological thinning and elliptic regression. Subsequently, the texture inside a potential defect shape is extracted and compared with the texture of the surrounding non-defect pavement in order to determine if the region of interest represents an actual pothole. This methodology has been implemented in a MATLAB prototype, trained and tested on 120 pavement images. The results show that this method can detect potholes in asphalt pavement images with reasonable accuracy.

An optimum feature extraction method based on Wavelet–Radon Transform and Dynamic Neural Network for pavement distress classification

Quantification of pavement crack data is one of the most important criteria in determining optimum pavement maintenance strategies. Recently, multi-resolution analysis such as wavelet decompositions provides very good multi-resolution analytical tools for different scales of pavement analysis and distresses classification. This paper present an automatic diagnosis system for detecting and classification pavement crack distress based on Wavelet–Radon Transform (WR) and Dynamic Neural Network (DNN) threshold selection. The algorithm of the proposed system consists of a combination of feature extraction using WR and classification using the neural network technique. The proposed WR + DNN system performance is compared with static neural network (SNN). In test stage; proposed method was applied to the pavement images database to evaluate the system performance. The correct classification rate (CCR) of proposed system is over 99%. This research demonstrated that the WR + DNN method can be used efficiently for fast automatic pavement distress detection and classification. The details of the image processing technique and the characteristic of system are also described in this paper.

A comparison of multi-resolution methods for detection and isolation of pavement distress

The research presented in this article is aimed at the development of an automated imaging system for distress detection and isolation in asphalt pavement distress obtained from pavement image acquisition system (PIAS). This article focuses on comparing the discriminating power of several multi-resolution texture analysis techniques using wavelet, ridgelet, and curvelet-based texture descriptors. The approach consists of four steps: Image collection, segmentation of regions of interest (ROI), extraction of the most discriminative texture features, creation of a classifier that automatically identifies the pavement distress, and storage. Tests comparing the wavelet, ridgelet, and curvelet texture features indicated that curvelet-based signatures outperform all other multi-resolution techniques for pothole distress, yielding accuracy rates in the 97.9%. Ridgelet-based signatures outperform all other multi-resolution techniques for cracking distress, yielding accuracy rates in the 93.6–96.4% rate.

Automatic Pavement Crack Detection Using Texture and Shape Descriptors

Pavement distress detection and analysis is the most important part of automated pavement inspection systems. Due to the circumstances such as complex texture, uneven illumination, and nonuniform background, pavement distress detection is not a simply edge detection process. Over the past 30 years, lots of methods were proposed to detect pavement distresses, especially cracks. In this letter, a novel automatic pavement crack detection approach based on texture analysis and shape descriptors is proposed. Pavement surface is seen as a texture surface, and distresses are defined as inhomogeneities occurring in the texture surface. Six texture features and two translation-invariant shape descriptors were used here as discriminate features against irregular texture and uneven illumination. By using a SVM classifier, all sub-images are classified as crack or non-crack. Final results were obtained after post-processing, which includes segmentation, fake-crack eliminating, and crack-measuring methods. Compared with a traditional edge detector such as a Canny operator, experimental results demonstrated that all cracks are correctly detected by the proposed method, even in a strong texture background or in the surface with uneven illumination.

Quantitative Performance Evaluation Algorithms for Pavement Distress Segmentation

Algorithms for pavement distress image segmentation are crucial to developing an automatic pavement distress detection and classification system. Many algorithms for pavement distress segmentation have been developed in the past decade; however, the lack of good methods to evaluate their performance quantitatively hinders the focused development of better segmentation algorithms. In this paper, a novel method is developed to quantitatively evaluate the performance of different pavement distress segmentation algorithms. This method uses the buffered Hausdorff distance to estimate the deviation of the cracks in the automatically segmented image from the ground truth cracks. The proposed method captures the local effectiveness of segmentation methods around the crack region without compromising its robustness to isolated pixel deviations caused by noise. Besides real pavement images, synthetic images simulating extreme pavement distress conditions are used to evaluate the capability of the proposed method and show its merits. The proposed method outperforms four other possible quantification methods and demonstrates its superior capability in providing a better score separation to distinguish the performance of different segmentation algorithms.

Critical Assessment of Pavement Distress Segmentation Methods

Image segmentation is the crucial step in automatic image distress detection and classification (e.g., types and severities) and has important applications for automatic crack sealing. Although many researchers have developed pavement distress detection and recognition algorithms, full automation has remained a challenge. This is the first paper that uses a scoring measure to quantitatively and objectively evaluate the performance of six different segmentation algorithms. Up-to-date research on pavement distress detection and segmentation is comprehensively reviewed to identify the research need. Six segmentation methods are then tested using a diverse set of actual pavement images taken on interstate highway I-75/I-85 near Atlanta and provided by the Georgia Department of Transportation with varying lighting conditions, shadows, and crack positions to differentiate their performance. The dynamic optimization-based method, which was previously used for segmenting low signal-to-noise ratio (SNR) digital radiography images, outperforms the other five methods based on our scoring measure. It is robust to image variations in our data set but the computation time required is high. By critically assessing the strengths and limitations of the existing algorithms, the paper provides valuable insight and guideline for future algorithm development that are important in automating image distress detection and classification.

Wavelet-based pavement distress detection and evaluation

An automated pavement inspection system consists of image acquisition and distress image processing. The former is accomplished with imaging sensors, such as video cameras and photomultiplier tubes. The latter includes distress detection, isolation, classification, evaluation, segmentation, and compression. We focus on wavelet-based distress detection, isolation, and evaluation. After a pavement image is decomposed into different-frequency subbands by the wavelet transform, distresses are transformed into high-amplitude wavelet coefficients and noise is transformed into low-amplitude wavelet coefficients, both in the high-frequency subbands, referred to as details. Background is transformed into wavelet coefficients in a low-frequency subband, referred to as approximation. First, several statistical criteria are developed for distress detection and isolation, which include the high-amplitude wavelet coefficient percentage (HAWCP), the high-frequency energy percentage (HFEP), and the standard deviation (STD). These criteria are tested on hundreds of pavement images differing by type, severity, and extent of distress. Experimental results demonstrate that the proposed criteria are reliable for distress detection and isolation and that real-time distress detection and screening is currently feasible. A norm for pavement distress quantification, which is defined as the product of HAWCP and HFEP, is also proposed. Experimental results show that the norm is a useful index for pavement distress evaluation.

Automated Real-Time Pavement Distress Analysis

Pavement distress detection and analysis is an important component of pavement management systems. Conventional visual and manual pavement distress analysis techniques are very costly, time-consuming, dangerous, labor-intensive, tedious, and subjective; have a high degree of variability; are unable to provide meaningful quantitative information; and almost always lead to inconsistencies in distress detail over space and across evaluations. In this paper, a novel system for multipurpose automated real-time pavement distress analysis based on fuzzy logic and morphology is presented. The proposed system provides high data acquisition rates; effectively and accurately identifies the type, severity, and extent of surface distress; improves the safety and efficiency of data collection; offers an objective standard of analysis and classification of distress; helps identify cost-effective maintenance and repair plans; provides images and examples through the information highway to other user-researchers; and provides an image and sample bank for training or as the benchmark for testing new algorithms. The proposed system can also contribute to other research in pavement maintenance, repair, and rehabilitation.