Automatic inspection of pavement cracking distress

Huang, Yun; Xu, Bugao

doi:10.1117/1.2177650

Cited by 173 publications

(77 citation statements)

References 2 publications

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“…[5], Huang and Xu [12], Li et al [18], Moussa and Hussain [20], Oliveira and Correia [23], Salman et al [31], Subirats et al [34], Tanaka and Uematsu [35],Varadharajan et al [38], Vivekanandreddy et al [39], Zou et al [43]). However, all of the methods mentioned above consider only one type of distress.…”

Section: State Of Research and Practice In The Pavement Distress Detementioning

confidence: 99%

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

Doycheva

Koch

König

2016

J Real-Time Image Proc

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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 towards 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 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 2 sensors and cameras in order to collect data related to pavement distress and record videos of the pavement surface.Yet, this data is usually not processed while driving, but instead it is recorded and later analyzed offline. 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.

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Section: State Of Research and Practice In The Pavement Distress Detementioning

confidence: 99%

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

Doycheva

Koch

König

2016

J Real-Time Image Proc

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“…The post processing of the images is done by AIES 6 (Monti 1995;Gavilán et al 2011) The Texas Department of Transportation developed the vehicle for data collection with the software, Vcarck. The vehicle is equipped with line scanning camera and the system for crack detection that can work at 112.5 km per sec (Huang & Xu 2006;Xu 2006). TRL in 2011 found that the ratio of true-negative crack detection is a bit high in ARAN and TRL system and Waylink systems.…”

Section: Automated Data Collection Vehiclesmentioning

confidence: 99%

Kinect, a Novel Cutting Edge Tool in Pavement Data Collection

Mahmoudzadeh

Yeganeh

Golroo

2015

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Pavement roughness and surface distress detection is of interest of decision makers due to vehicle safety, user satisfaction, and cost saving. Data collection, as a core of pavement management systems, is required for these detections. There are two major types of data collection: traditional/manual data collection and automated/semi-automated data collection. This paper study different non-destructive tools in detecting cracks and potholes. For this purpose, automated data collection tools, which have been utilized recently are discussed and their applications are criticized. The main issue is the significant amount of money as a capital investment needed to buy the vehicle. The main scope of this paper is to study the approach and related tools that not only are cost-effective but also precise and accurate. The new sensor called Kinect has all of these specifications. It can capture both RGB images and depth which are of significant use in measuring cracks and potholes. This sensor is able to take image of surfaces with adequate resolution to detect cracks along with measurement of distance between sensor and obstacles in front of it which results in depth of defects. This technology has been very recently studied by few researchers in different fields of studies such as project management, biomedical engineering, etc. Pavement management has not paid enough attention to use of Kinect in monitoring and detecting distresses. This paper is aimed at providing a thorough literature review on usage of Kinect in pavement management and finally proposing the best approach which is cost-effective and precise.

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“…Data for the vertical direction of crack progressing direction was obtained with sub-pixel accuracy by using Bspine interpolation (2). B-spline function is robust to against gaussian noise being distributed around crack [5].…”

Section: ) Crack Data Acquisitionmentioning

confidence: 99%

Intelligent Bridge Inspection Using Remote Controlled Robot and Image Processing Technique

Lee¹,

Shin²,

Seo³

et al. 2011

Proceedings of the International Symposium on Automation and Robotics in Construction (IAARC)

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ABSTRACT:In this paper, we discuss an intelligent bridge inspection system by using a Robot and IT technology which enables acquiring images of the bridge condition for managing the safety of its structure. The purpose of this study is composed of two parts: 1) the image acquisition of a bridge structure with the application of a vision-based robot, the name of the robot system is U-BIROS(Ubiquitous Bridge Inspection Robot System) which is remote controlled. 2) Development of digital image processing software for assessing condition of the bridge, and measuring the size of any crack captured in the images. The robot arm is designed to operate on the underside of superstructure during inspections, and equipped with vision devices (digital camera, lens, and lights). The domain of a superstructure has been divided into appropriate segments to facilitate the image acquiring task by making its total domain irrelevant to the procedure. The system's software detects, measures the width and length of a crack semi-automatically, and also composes a drawing map of the whole area of the bridge from the crack's data for a user's convenience in determining the status of bridge. Through field experiments, the application of this inspection system with specialty software has proven to be much faster, safer, and reliable than the inspections carried out by the naked eyes in managing safety of the bridges.

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Automatic inspection of pavement cracking distress

Cited by 173 publications

References 2 publications

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

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

Kinect, a Novel Cutting Edge Tool in Pavement Data Collection

Intelligent Bridge Inspection Using Remote Controlled Robot and Image Processing Technique

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