Use of the Pavement Surface Cracking Metric to Quantify Distresses from Digital Images

2021 ◽  
pp. 036119812110081
Author(s):  
Danilo Balzarini ◽  
James Erskine ◽  
Michael Nieminen
Keyword(s):  
Data Collection ◽  
Digital Images ◽  
Physical Property ◽  
Pavement Distress ◽  
Surface Cracking ◽  
Pavement Surface ◽  
Laser Technologies ◽  
Pavement Distresses ◽  
Repeatability And Reproducibility

The development of new laser technologies in recent years has changed pavement data collection, opening the door to a fully automated approach. In this paper the application of the Pavement Surface Cracking Metric (PSCM), inspired by the Universal Cracking Indicator proposed by William Paterson in 1994, and developed by the ASTM E17 group is presented. The method uses quantitative definitions to ensure consistency of the results and eliminate the subjectivity associated with human ratings of pavement distresses. Multiple runs of pavement data have been collected on three asphalt sections to assess the repeatability and reproducibility of the method. The application of the Pavement Surface Cracking Index to convert the PSCM value, which is a physical property of the pavement, into a 100-0 score of the pavement section is also presented. Finally, the use of the PSCM to classify pavement distress and the inclusion of potholes and patching in the metrics are discussed.

scholarly journals Attention-Based Multi-Scale Convolutional Neural Network (A+MCNN) for Multi-Class Classification in Road Images

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 5137
Author(s):  
Elham Eslami ◽  
Hae-Bum Yun
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Contextual Information ◽  
Automated Classification ◽  
Automated Recognition ◽  
Pavement Distress ◽  
Multi Scale ◽  
Pavement Distresses ◽  
Multi Class Classification ◽  
Transportation Applications

Automated pavement distress recognition is a key step in smart infrastructure assessment. Advances in deep learning and computer vision have improved the automated recognition of pavement distresses in road surface images. This task remains challenging due to the high variation of defects in shapes and sizes, demanding a better incorporation of contextual information into deep networks. In this paper, we show that an attention-based multi-scale convolutional neural network (A+MCNN) improves the automated classification of common distress and non-distress objects in pavement images by (i) encoding contextual information through multi-scale input tiles and (ii) employing a mid-fusion approach with an attention module for heterogeneous image contexts from different input scales. A+MCNN is trained and tested with four distress classes (crack, crack seal, patch, pothole), five non-distress classes (joint, marker, manhole cover, curbing, shoulder), and two pavement classes (asphalt, concrete). A+MCNN is compared with four deep classifiers that are widely used in transportation applications and a generic CNN classifier (as the control model). The results show that A+MCNN consistently outperforms the baselines by 1∼26% on average in terms of the F-score. A comprehensive discussion is also presented regarding how these classifiers perform differently on different road objects, which has been rarely addressed in the existing literature.

Repeatability, Reproducibility, and Calibration of the MyotonPro® on Tissue Mimicking Phantoms

2013 ◽  
Author(s):  
John Dougherty ◽  
Emily Schaefer ◽  
Kalyani Nair ◽  
Joseph Kelly ◽  
Alfonse Masi
Keyword(s):  
Data Collection ◽  
Dynamic Stiffness ◽  
Correlation Study ◽  
Living Tissue ◽  
Polymeric Gel ◽  
Stiffness Properties ◽  
Tissue Phantoms ◽  
Repeatability And Reproducibility ◽  
Living Tissues

The MyotonPro® (Myoton AS, Tallinn, Estonia) is commonly used to quantify stiffness properties of living tissues in situ. Current studies quantify the dynamic stiffness properties of living tissues, but do not validate or compare these measurements to a standardized method. Additionally, living tissue, being dynamic in nature, presents much variability in data collection. To address these issues this study focuses on the repeatability and reproducibility of the MyotonPro® on polymeric gel-based tissue phantoms. In addition, a correlation study is also performed to translate dynamic stiffness to a more standardized property, Young’s modulus. Such studies help to confirm the reliability of the measurements obtained in situ.

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 Designing a Prototype of Digital Museum to Promote Woven Songket, A Local Product of Sumatera, Indonesia

2014 ◽  
Vol 68 (3) ◽  
Author(s):  
Bertalya Bertalya ◽  
Prihandoko Prihandoko ◽  
Rakhma Oktavina ◽  
Danu Satria Ramadhan
Keyword(s):  
Data Collection ◽  
Cultural Heritage ◽  
Digital Images ◽  
Human Life ◽  
Creative Industries ◽  
Web Pages ◽  
Digital Museum ◽  
Local Product ◽  
Wide Range ◽  
Analysis Design

One of Indonesia's cultural heritage is woven songket. Songket is not just a piece of cloth but with a diversity of functions and motifs contained profound philosophy about human life. Hence, the songket fabric must be preserved so as not to disappear due to the influx of modern cloth products from outside.This study aims to create a digital museum that displays digital images of woven songket and provide information relating to the various motifs representing the cultural distinctiveness of local centers in  Sumatra. In addition, a digital gallery is developed to display a wide range of products derived from the local creative industries of songket cloth. The methods used for data collection are observation and interview. The development of digital museum consists of the process of formulation, planning, analysis, design, manufacture and testing of web pages, as well as evaluation.

Contracting for Pavement Distress Data Collection

1998 ◽  
Vol 1643 (1) ◽  
pp. 80-85 ◽  
Author(s):  
Roger E. Smith ◽  
Thomas J. Freeman ◽  
Olga J. Pendleton
Keyword(s):  
Quality Control ◽  
Quality Assurance ◽  
Data Collection ◽  
Pavement Management ◽  
Management Systems ◽  
Automated Data Collection ◽  
Pavement Distress ◽  
Data Collection Methods ◽  
Pavement Management Systems ◽  
Collection Methods

Many agencies responsible for managing pavements have adopted pavement management systems (PMS) to help manage their pavement networks more cost-effectively. One of the most costly parts of operating a PMS is collecting condition information, especially pavement distress information. Many agencies have started to contract for pavement distress data collection. Some of the agencies have experienced problems with the data collected by contract. A study for agencies in Washington and Oregon to define the accuracy of data needed by the agencies with an evaluation of certain participating vendors using semiautomated data collection methods is described. Issues about quality control and quality assurance faced by agencies considering contracting for automated data collection also are raised. These issues need additional study to develop appropriate guidelines. The initial set provided is based on discussions with some of the agencies currently contracting for pavement distress data collection.

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