Metrology and visualization of potholes using the microsoft kinect sensor

Moazzam, Imran; Kamal, Khurram; Mathavan, Senthan; Usman, Sahnius; Rahman, Mujib

doi:10.1109/itsc.2013.6728408

Cited by 85 publications

(30 citation statements)

References 4 publications

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“…Research by input data and data collection. [2, Image [35][36][37] Radar [38][39][40][41][42][43][44][45][46] 3D images or point clouds [47,48] Acoustic…”

Section: Literature Reviewmentioning

confidence: 99%

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Pavement Distress Detection with Deep Learning Using the Orthoframes Acquired by a Mobile Mapping System

et al. 2019

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The subject matter of this research article is automatic detection of pavement distress on highway roads using computer vision algorithms. Specifically, deep learning convolutional neural network models are employed towards the implementation of the detector. Source data for training the detector come in the form of orthoframes acquired by a mobile mapping system. Compared to our previous work, the orthoframes are generally of better quality, but more importantly, in this work, we introduce a manual preprocessing step: sets of orthoframes are carefully selected for training and manually digitized to ensure adequate performance of the detector. Pretrained convolutional neural networks are then fine-tuned for the problem of pavement distress detection. Corresponding experimental results are provided and analyzed and indicate a successful implementation of the detector.

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“…Research by input data and data collection. [2, Image [35][36][37] Radar [38][39][40][41][42][43][44][45][46] 3D images or point clouds [47,48] Acoustic…”

Section: Literature Reviewmentioning

confidence: 99%

“…Additionally, pavement distress detection research can be categorized based on which defects are detected. While most research is aimed at detecting cracks (with or without other defects), some approaches, such as [40,41,54], focus solely on detecting potholes.…”

Section: Source Input Data and Data Collectionmentioning

confidence: 99%

Pavement Distress Detection with Deep Learning Using the Orthoframes Acquired by a Mobile Mapping System

et al. 2019

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“…Stereo vision methods need a high computational effort to reconstruct pavement surfaces through matching feature points between two views so that it is difficult to use them in a real-time environment [7,8,10,11,[13][14][15]. Recently, Moazzam et al [17] used a low-cost Kinect sensor to collect the pavement depth images and calculate the approximate volume of a pothole. Although it is cost-effective as compared with industrial cameras and lasers, the use of infrared technology based on a Kinect sensor for measurement is still a novel idea, and further research is necessary for improvement in error rates.…”

Section: Literature Reviewmentioning

confidence: 99%

Image-Based Pothole Detection System for ITS Service and Road Management System

Ryu

Kim

2015

Mathematical Problems in Engineering

120

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Potholes can generate damage such as flat tire and wheel damage, impact and damage of lower vehicle, vehicle collision, and major accidents. Thus, accurately and quickly detecting potholes is one of the important tasks for determining proper strategies in ITS (Intelligent Transportation System) service and road management system. Several efforts have been made for developing a technology which can automatically detect and recognize potholes. In this study, a pothole detection method based on twodimensional (2D) images is proposed for improving the existing method and designing a pothole detection system to be applied to ITS service and road management system. For experiments, 2D road images that were collected by a survey vehicle in Korea were used and the performance of the proposed method was compared with that of the existing method for several conditions such as road, recording, and brightness. The results are promising, and the information extracted using the proposed method can be used, not only in determining the preliminary maintenance for a road management system and in taking immediate action for their repair and maintenance, but also in providing alert information of potholes to drivers as one of ITS services.

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“…At last, the approximate volume of the pothole was proposed using trapezoidal rule. The algorithm has 15 % error with respect to actual data (Moazzam et al 2013). Deon Joubert et al in 2011 present a cost effective system that can be mount into the vehicle for detection and analyzing of a pothole at maximum speed of 60Km per sec.…”

Section: Utilize Kinect In Pavement Managementmentioning

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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Metrology and visualization of potholes using the microsoft kinect sensor

Cited by 85 publications

References 4 publications

Pavement Distress Detection with Deep Learning Using the Orthoframes Acquired by a Mobile Mapping System

Pavement Distress Detection with Deep Learning Using the Orthoframes Acquired by a Mobile Mapping System

Image-Based Pothole Detection System for ITS Service and Road Management System

Kinect, a Novel Cutting Edge Tool in Pavement Data Collection

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