Adaptive Road Crack Detection System by Pavement Classification

Gavilán, Miguel Urrestarazu; Balcones, David Vivas; Marcos, O.; Llorca, David Fernández; Sotelo, Miguel Ángel; Parra, I.; Ocaña, Manuel; Aliseda, Pedro; Yarza, Pablo; Amírola, Alejandro

doi:10.3390/s111009628

Cited by 311 publications

(177 citation statements)

References 43 publications

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“…The area scanning cameras are used for capturing the detections images which can be analysed by 3 Pavement Management System 4 Australian Commonwealth and Industrial Research Organization 5 Automated Road Analyser WiseCrax software. The vehicle could collect up to 15 different data type, at the highway speed (the minimum speed for the machine is 24 km per sec) (Gavilán et al 2011). PAVUE system is widely used in Europe which used multiple video cameras or line scanning cameras for capturing the defection's images.…”

Section: Automated Data Collection Vehiclesmentioning

confidence: 99%

“…The first company which has developed the automated crack data collection system was CSIRO 4 .The vehicle can detect the cracks which are wider than 1 mm by digital camera at highway speed by RoadCrack software (Gavilán et al 2011). The vehicle is applicable during night time due to the availability of light equipment in the vehicle.…”

Section: Automated Data Collection Vehiclesmentioning

confidence: 99%

“…PAVUE system is widely used in Europe which used multiple video cameras or line scanning cameras for capturing the defection's images. 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).…”

Section: Automated Data Collection Vehiclesmentioning

confidence: 99%

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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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Section: Automated Data Collection Vehiclesmentioning

confidence: 99%

Section: Automated Data Collection Vehiclesmentioning

confidence: 99%

Section: Automated Data Collection Vehiclesmentioning

confidence: 99%

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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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“…However, in the end, they have learned that too many sensitive parameters are needed and a fully automatic approach is not available. Gavilán et al(2011) adopted two line scanner to acquire road pavement image and suggest linear SVM for the recognition of crack. Abdel-Qader et al(2003) utilize Fast Haar Transform (FHT), Fast Fourier Transform, Sobel, and Canny for crack detection.…”

Section: Introductionmentioning

confidence: 99%

Bridge Crack Detection Using Multi-Rotary Uav and Object-Base Image Analysis

Rau

Hsiao

Jhan

et al. 2017

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

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ABSTRACT:Bridge is an important infrastructure for human life. Thus, the bridge safety monitoring and maintaining is an important issue to the government. Conventionally, bridge inspection were conducted by human in-situ visual examination. This procedure sometimes require under bridge inspection vehicle or climbing under the bridge personally. Thus, its cost and risk is high as well as labor intensive and time consuming. Particularly, its documentation procedure is subjective without 3D spatial information. In order cope with these challenges, this paper propose the use of a multi-rotary UAV that equipped with a SONY A7r2 high resolution digital camera, 50 mm fixed focus length lens, 135 degrees up-down rotating gimbal. The target bridge contains three spans with a total of 60 meters long, 20 meters width and 8 meters height above the water level. In the end, we took about 10,000 images, but some of them were acquired by hand held method taken on the ground using a pole with 2-8 meters long. Those images were processed by Agisoft PhotoscanPro to obtain exterior and interior orientation parameters. A local coordinate system was defined by using 12 ground control points measured by a total station. After triangulation and camera self-calibration, the RMS of control points is less than 3 cm. A 3D CAD model that describe the bridge surface geometry was manually measured by PhotoscanPro. They were composed of planar polygons and will be used for searching related UAV images. Additionally, a photorealistic 3D model can be produced for 3D visualization. In order to detect cracks on the bridge surface, we utilize object-based image analysis (OBIA) technique to segment the image into objects. Later, we derive several object features, such as density, area/bounding box ratio, length/width ratio, length, etc. Then, we can setup a classification rule set to distinguish cracks. Further, we apply semi-global-matching (SGM) to obtain 3D crack information and based on image scale we can calculate the width of a crack object. For spalling volume calculation, we also apply SGM to obtain dense surface geometry. Assuming the background is a planar surface, we can fit a planar function and convert the surface geometry into a DSM. Thus, for spalling area its height will be lower than the plane and its value will be negative. We can thus apply several image processing technique to segment the spalling area and calculate the spalling volume as well. For bridge inspection and UAV image management within a laboratory, we develop a graphic user interface. The major functions include crack auto-detection using OBIA, crack editing, i.e. delete and add cracks, crack attributing, 3D crack visualization, spalling area/volume calculation, bridge defects documentation, etc.

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“…The pavement images can provide information on the presence of cracks through the pixel intensities and the shape of the darker image features. Many image processing techniques exist for the detection of cracking on grey level images, e.g., [4][5][6][7][8][9][10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Ongoing tests and improvements of the MPS algorithm for the automatic crack detection within grey level pavement images

Baltazart¹,

Nicolle²,

Yang

2017

2017 25th European Signal Processing Conference (EUSIPCO)

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Abstract-The MPS approach (Minimal Path Selection) has shown in [1] to provide robust and accurate segmentation of cracks within pavement images compared to other algorithms. As a counterpart, MPS suffers from a large computing time. In this paper, we present three different ongoing improvements to reduce the computing time and to improve the overall segmentation performance. Most of the work focuses on the first three steps of the algorithm which achieve the segmentation of the crack skeleton. This is at first the improvement of the MPS methodology under Matlab coding, then, the C language MPS version and finally, the first attempt to parallelize MPS under the GPU platform. The results on pavement images illustrate the achieved improvements in terms of better segmentation and faster computational time.

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Adaptive Road Crack Detection System by Pavement Classification

Cited by 311 publications

References 43 publications

Kinect, a Novel Cutting Edge Tool in Pavement Data Collection

Kinect, a Novel Cutting Edge Tool in Pavement Data Collection

Bridge Crack Detection Using Multi-Rotary Uav and Object-Base Image Analysis

Ongoing tests and improvements of the MPS algorithm for the automatic crack detection within grey level pavement images

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