Image-based 3D reconstruction using traditional and UAV datasets for analysis of road pavement distress

2018 ◽  
Vol 96 ◽  
pp. 457-469 ◽  
Author(s):  
Laura Inzerillo ◽  
Gaetano Di Mino ◽  
Ronald Roberts
Keyword(s):  
3D Reconstruction ◽  
Pavement Distress ◽  
Road Pavement

scholarly journals Pavement Distress Detection Methods: A Review

2018 ◽  
Vol 3 (4) ◽  
pp. 58 ◽  
Author(s):  
Antonella Ragnoli ◽  
Maria De Blasiis ◽  
Alessandro Di Benedetto
Keyword(s):  
Data Collection ◽  
Operating Cost ◽  
Pavement Management ◽  
Point Of View ◽  
Detection Methods ◽  
Technical Improvement ◽  
Pavement Distress ◽  
The Road ◽  
Road Pavement ◽  
Pavement Distress Detection

The road pavement conditions affect safety and comfort, traffic and travel times, vehicles operating cost, and emission levels. In order to optimize the road pavement management and guarantee satisfactory mobility conditions for all road users, the Pavement Management System (PMS) is an effective tool for the road manager. An effective PMS requires the availability of pavement distress data, the possibility of data maintenance and updating, in order to evaluate the best maintenance program. In the last decade, many researches have been focused on pavement distress detection, using a huge variety of technological solutions for both data collection and information extraction and qualification. This paper presents a literature review of data collection systems and processing approach aimed at the pavement condition evaluation. Both commercial solutions and research approaches have been included. The main goal is to draw a framework of the actual existing solutions, considering them from a different point of view in order to identify the most suitable for further research and technical improvement, while also considering the automated and semi-automated emerging technologies. An important attempt is to evaluate the aptness of the data collection and extraction to the type of distress, considering the distress detection, classification, and quantification phases of the procedure.

scholarly journals A potential crack region method to detect crack using image processing of multiple thresholding

2022 ◽  
Author(s):  
Cheng Chen ◽  
Hyungjoon Seo ◽  
ChangHyun Jun ◽  
Yang Zhao
Keyword(s):  
Treatment Algorithm ◽  
Histogram Equalization ◽  
Pavement Distress ◽  
Region Method ◽  
Road Pavement ◽  
Traffic Lane ◽  
Local Threshold ◽  
Global Threshold ◽  
Segmentation Methods ◽  
Pre Treatment

AbstractIn this paper, a potential crack region method is proposed to detect road pavement cracks by using the adaptive threshold. To reduce the noises of the image, the pre-treatment algorithm was applied according to the following steps: grayscale processing, histogram equalization, filtering traffic lane. From the image segmentation methods, the algorithm combines the global threshold and the local threshold to segment the image. According to the grayscale distribution characteristics of the crack image, the sliding window is used to obtain the window deviation, and then, the deviation image is segmented based on the maximum inter-class deviation. Obtain a potential crack region and then perform a local threshold-based segmentation algorithm. Real images of pavement surface were used at the Su Tong Li road in Suzhou, China. It was found that the proposed approach could give a more explicit description of pavement cracks in images. The method was tested on 509 images of the German asphalt pavement distress (Gap) dataset: The test results were found to be promising (precision = 0.82, recall = 0.81, F1 score = 0.83).

scholarly journals Comparison of Fuzzy AHP and Fuzzy TOPSIS for Road Pavement Maintenance Prioritization: Methodological Exposition and Case Study

2015 ◽  
Vol 2015 ◽  
pp. 1-17 ◽  
Author(s):  
Yashon O. Ouma ◽  
J. Opudo ◽  
S. Nyambenya
Keyword(s):  
Fuzzy Ahp ◽  
Pairwise Comparison ◽  
Fuzzy Topsis ◽  
Subjective Analysis ◽  
Pavement Maintenance ◽  
Pavement Distress ◽  
Road Pavement ◽  
Study Results ◽  
Order Preference

For road pavement maintenance and repairs prioritization, a multiattribute approach that compares fuzzy Analytical Hierarchy Process (AHP) and fuzzy Technique for Order Preference by Ideal Situation (TOPSIS) is evaluated. The pavement distress data was collected through empirical condition surveys and rating by pavement experts. In comparison to the crisp AHP, the fuzzy AHP and fuzzy TOPSIS pairwise comparison techniques are considered to be more suitable for the subjective analysis of the pavement conditions for automated maintenance prioritization. From the case study results, four pavement maintenance objectives were determined as road safety, pavement surface preservation, road operational status and standards, and road aesthetics, with corresponding depreciating significance weights ofW=0.37,0.31,0.22,0.10T. The top three maintenance functions were identified as Thin Hot Mix Asphalt (HMA) overlays, resurfacing and slurry seals, which were a result of pavement cracking, potholes, raveling, and patching, while the bottom three were cape seal, micro surfacing, and fog seal. The two methods gave nearly the same prioritization ranking. In general, the fuzzy AHP approach tended to overestimate the maintenance prioritization ranking as compared to the fuzzy TOPSIS.

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

2016 ◽  
Vol XLI-B1 ◽  
pp. 689-694 ◽  
Author(s):  
A. Miraliakbari ◽  
S. Sok ◽  
Y. O. Ouma ◽  
M. Hahn
Keyword(s):  
Comparative Evaluation ◽  
Crack Detection ◽  
Pavement Condition ◽  
Pavement Distress ◽  
The Road ◽  
Road Pavement ◽  
Segmentation Methods ◽  
Increasing Demand ◽  
Rotational Invariant ◽  
Pavement Crack Detection

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.

scholarly journals Pavement Distress Detection Methods: A Review

2018 ◽  
Author(s):  
Antonella Ragnoli ◽  
Maria Rosaria De Blasiis ◽  
Alessandro Di Benedetto
Keyword(s):  
Data Collection ◽  
Operating Cost ◽  
Pavement Management ◽  
Detection Methods ◽  
Technical Improvement ◽  
Pavement Condition ◽  
Pavement Distress ◽  
The Road ◽  
Road Pavement ◽  
Pavement Distress Detection

The road pavement condition affects safety and comfort, traffic and travel times, vehicles operating cost and emission levels. In order to optimize the road pavement management and guarantee satisfactory mobility conditions for all the road users, the Pavement Management System (PMS) is an effective tool for the road manager. An effective PMS requires the availability of pavement distress data, the possibility of data maintenance and updating, in order to evaluate the best maintenance program. In the last decade, many researches have been focused on pavement distress detection, using a huge variety of technological solutions for both data collection and information extraction and qualification. This paper presents a literature review of data collection systems and processing approach aimed at the pavement condition evaluation. Both commercial solutions and research approaches have been included. The main goal is to draw a framework of the actual existing solutions, considering them from a different point of view in order to identify the most suitable for further research and technical improvement, also considering the automated and semi-automated emerging technologies. An important attempt is to evaluate the aptness of the data collection and extraction to the type of distress, considering the distress detection, classification and quantification phases of the procedure.

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

2016 ◽  
Vol XLI-B1 ◽  
pp. 689-694
Author(s):  
A. Miraliakbari ◽  
S. Sok ◽  
Y. O. Ouma ◽  
M. Hahn
Keyword(s):  
Comparative Evaluation ◽  
Crack Detection ◽  
Pavement Condition ◽  
Pavement Distress ◽  
The Road ◽  
Road Pavement ◽  
Segmentation Methods ◽  
Increasing Demand ◽  
Rotational Invariant ◽  
Pavement Crack Detection

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.

scholarly journals Exploiting Low-Cost 3D Imagery for the Purposes of Detecting and Analyzing Pavement Distresses

2020 ◽  
Vol 5 (1) ◽  
pp. 6 ◽  
Author(s):  
Ronald Roberts ◽  
Laura Inzerillo ◽  
Gaetano Di Mino
Keyword(s):  
Mobile Phones ◽  
Low Cost ◽  
Point Clouds ◽  
3D Models ◽  
Pavement Management ◽  
Physical Parameters ◽  
Pavement Distress ◽  
Road Pavement ◽  
Significant Research ◽  
Pavement Distresses

Road pavement conditions have significant impacts on safety, travel times, costs, and environmental effects. It is the responsibility of road agencies to ensure these conditions are kept in an acceptable state. To this end, agencies are tasked with implementing pavement management systems (PMSs) which effectively allocate resources towards maintenance and rehabilitation. These systems, however, require accurate data. Currently, most agencies rely on manual distress surveys and as a result, there is significant research into quick and low-cost pavement distress identification methods. Recent proposals have included the use of structure-from-motion techniques based on datasets from unmanned aerial vehicles (UAVs) and cameras, producing accurate 3D models and associated point clouds. The challenge with these datasets is then identifying and describing distresses. This paper focuses on utilizing images of pavement distresses in the city of Palermo, Italy produced by mobile phone cameras. The work aims at assessing the accuracy of using mobile phones for these surveys and also identifying strategies to segment generated 3D imagery by considering the use of algorithms for 3D Image segmentation to detect shapes from point clouds to enable measurement of physical parameters and severity assessment. Case studies are considered for pavement distresses defined by the measurement of the area affected such as different types of cracking and depressions. The use of mobile phones and the identification of these patterns on the 3D models provide further steps towards low-cost data acquisition and analysis for a PMS.

scholarly journals An innovative approach for high-performance road pavement monitoring using black box

2021 ◽  
Author(s):  
Monica Meocci ◽  
Valentina Branzi ◽  
Andrea Sangiovanni
Keyword(s):  
High Performance ◽  
Laser Scanning ◽  
Poor Quality ◽  
Vertical Acceleration ◽  
Pavement Distress ◽  
The Road ◽  
Road Pavement ◽  
Pavement Monitoring ◽  
Global Condition ◽  
On The Road

AbstractOne of the criteria adopted by the Word Bank with the aim of defining the economic level of a country is represented by the condition of the road pavements. To ensure adequate road pavement quality, road authorities should be continuously monitoring and repair the detected anomalies. To fast solve problems associated with poor quality of road surface such as comfort or safety, the presence of distress must be detected quickly. The high-performance pavement distress detection, such as those base on the image processing or on the laser scanning, is very expensive and does not allow to the road administration to conduct the appropriate monitoring campaigns. To solve these problems, the paper describes the pave box methodology, an innovative and immediately operational distress detection approach based on the exploitation of data collected by the black boxes located inside the vehicles that routinely pass on the road network. Data processing and the algorithms used in the post-processing evaluation of the vertical acceleration were compared with existing visual surveys procedures such as PCI. Two different indices have been proposed to detect and classify both the local damages and the global condition of the entire road. Pave box provides a robust evaluation of the pavement condition that allows to detect all the severe distress and not less than 70% of the minor damages on the pavement surface. The proposal is characterized by low time and cost consumption and it represents an effective tool for road authorities.

Detection, Averaging, and 3D Reconstruction of Biological Specimens on Hypercubes (Transputer-based) Computers

1990 ◽  
Vol 48 (1) ◽  
pp. 454-455
Author(s):  
Jose-Maria Carazo ◽  
I. Benavides ◽  
S. Marco ◽  
J.L. Carrascosa ◽  
E.L. Zapata
Keyword(s):  
3D Reconstruction ◽  
3D Structure ◽  
Three Dimensional ◽  
Software Tools ◽  
Mapping Method ◽  
Computer Architectures ◽  
Parallel Computer ◽  
Reconstruction Process ◽  
Computing Power ◽  
Order Of Magnitude

Obtaining the three-dimensional (3D) structure of negatively stained biological specimens at a resolution of, typically, 2 - 4 nm is becoming a relatively common practice in an increasing number of laboratories. A combination of new conceptual approaches, new software tools, and faster computers have made this situation possible. However, all these 3D reconstruction processes are quite computer intensive, and the middle term future is full of suggestions entailing an even greater need of computing power. Up to now all published 3D reconstructions in this field have been performed on conventional (sequential) computers, but it is a fact that new parallel computer architectures represent the potential of order-of-magnitude increases in computing power and should, therefore, be considered for their possible application in the most computing intensive tasks.We have studied both shared-memory-based computer architectures, like the BBN Butterfly, and local-memory-based architectures, mainly hypercubes implemented on transputers, where we have used the algorithmic mapping method proposed by Zapata el at. In this work we have developed the basic software tools needed to obtain a 3D reconstruction from non-crystalline specimens (“single particles”) using the so-called Random Conical Tilt Series Method. We start from a pair of images presenting the same field, first tilted (by ≃55°) and then untilted. It is then assumed that we can supply the system with the image of the particle we are looking for (ideally, a 2D average from a previous study) and with a matrix describing the geometrical relationships between the tilted and untilted fields (this step is now accomplished by interactively marking a few pairs of corresponding features in the two fields). From here on the 3D reconstruction process may be run automatically.

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