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Pavement profile analysis is a major component in pavement infrastructure management decision making for maintenance and rehabilitation. This paper takes an in-depth look at pavement profile characterization and evaluation; taking into account the inherent nature of road profile data i.e. non-stationary and non- Gaussian. Although there have been several studies aimed at the analysis and characterization of pavement profile, the bulk have been limited to applying relatively conventional signal processing techniques, such as the Fourier analysis. Using this approach, only the average condition of the local conditions can be represented; most transient and changing signals will not be handled well due to the averaging effect of the technique. The Hilbert-Huang transform operates at the scale of every oscillation, an extremely important property for obtaining localized profile information. In this work, the different algorithms of the Hilbert-Huang transform: Empirical Mode Decomposition (EMD), Ensemble Empirical Mode Decomposition (EEMD) and Complex Empirical Mode Decomposition (CEMD) have been discussed and implemented to extract useful information from road profile data. The robustness of the algorithms is compared based on its ability to produce physically meaningful Intrinsic Mode Functions (IMFs) which truly characterize the underlying process. The results show that although all the methodologies yielded similar residual trends, the CEMD produced physically meaningful and trusted IMFs whose information at the various levels of decomposition could be used to extract profile information such as the extent of deterioration and localized roughness information.
In this thesis, a novel, fast and self-adaptive image processing method is proposed for the extraction and connection of break points of cracks in pavement images. The algorithm first finds the initial point of a crack and then determines the crack's classification into transverse and longitudinal types. Different search algorithms are used for different types of cracks. Then the algorithm traces along the crack pixels to find the break point and then connect the identified crack point to the nearest break point in the particular search area. The nearest point then becomes the new initial point and the algorithm continues the process until reaching the end of the crack. The experimental results show that this connection algorithm is very effective in maximizing the accuracy of crack identification.
At head of title: National Cooperative Highway Research Program.
This thesis presents an automated pavement crack detection and classification system via image processing and pattern recognition algorithms. Pavement crack detection is important to the Departments of Transportation around the country as it is directly related to maintenance of pavement quality. Manual inspection and analysis of pavement distress is the prevalent method for monitoring pavement quality. However, inspecting miles of highway sections and analyzing each is a cumbersome and time consuming process. Hence, there has been research into automating the system of crack detection. In this thesis, an automated crack detection and classification algorithm is presented. The algorithm is built around the statistical tool of Principal Component Analysis (PCA). The application of PCA on images yields the primary features of cracks based on which, cracked images are distinguished from non-cracked ones. The algorithm consists of three levels of classification: a) pixel-level b) subimage (32 X 32 pixels) level and c) image level. Initially, at the lowermost level, pixels are classified as cracked/non-cracked using adaptive thresholding. Then the classified pixels are grouped into subimages, for reducing processing complexity. Following the grouping process, the classification of subimages is validated based on the decision of a Bayes classifier. Finally, image level classification is performed based on a subimage profile generated for the image. Following this stage, the cracks are further classified as sealed/unsealed depending on the number of sealed and unsealed subimages. This classification is based on the Fourier transform of each subimage. The proposed algorithm detects cracks aligned in longitudinal as well as transverse directions with respect to the wheel path with high accuracy. The algorithm can also be extended to detect block cracks, which comprise of a pattern of cracks in both alignments.
The signal processing task is a very critical issue in the majority of new technological inventions and challenges in a variety of applications in both science and engineering fields. Classical signal processing techniques have largely worked with mathematical models that are linear, local, stationary, and Gaussian. They have always favored closed-form tractability over real-world accuracy. These constraints were imposed by the lack of powerful computing tools. During the last few decades, signal processing theories, developments, and applications have matured rapidly and now include tools from many areas of mathematics, computer science, physics, and engineering. This book is targeted primarily toward both students and researchers who want to be exposed to a wide variety of signal processing techniques and algorithms. It includes 27 chapters that can be categorized into five different areas depending on the application at hand. These five categories are ordered to address image processing, speech processing, communication systems, time-series analysis, and educational packages respectively. The book has the advantage of providing a collection of applications that are completely independent and self-contained; thus, the interested reader can choose any chapter and skip to another without losing continuity.
Videotapes of highway pavement surfaces are collected with the Automatic Road Analyzer (ARAN). The videotapes are then brought back to the lab for visual evaluation. Therefore, there exists a need to automatically determine the type and extent of cracking by computerized means. The report describes the image analysis algorithm to classify the cracking of Asphalt-concrete Pavement (ACP) and Continuously Reinforced Concrete Pavement (CRCP). The image analysis software features a three-pass approach. The first pass detects crack segments from the analysis of the block projection histogram. The second pass re-examines the vicinity of the detected edge segments to locate the remaining thinner crack segments with less stringent rules. The third pass is to classify the cracking type based on the position alignment and orientation of the crack segments in the edge map.