A real-time 3D scanning system for pavement rutting and pothole detections

Li, Qingguang; Yao, Ming; Yao, Xun; Yu, Wangjie; Xu, Bugao

doi:10.1117/12.824559

Cited by 5 publications

(11 citation statements)

References 4 publications

(6 reference statements)

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“…F-measure is the harmonic mean of precision and recall, where an F-measure reaches its best value at 1 and worst at 0. The definitions of precision, recall, and F-measure are shown in = + In (12) and 13, TP denotes true positives; that is, pixels labeled as crack pixels in the ground truth are correctly recognized as crack pixels; FP denotes false positives; that is, pixels labeled as noncrack pixels in the ground truth are incorrectly recognized as crack pixels; FN represents false negatives; that is, pixels labeled as crack pixels in the ground truth are incorrectly detected as noncrack pixels. The ground truths of cracks were obtained by two steps: at the first step, crack maps were generated automatically by applying method proposed by Zhang [34]; at the second step, manual labeling was used to refine the crack maps provided by the first step.…”

Section: Experimental Results and Comparisonmentioning

confidence: 99%

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Automatic Segmentation and Enhancement of Pavement Cracks Based on 3D Pavement Images

Wang

Zhang

et al. 2019

Journal of Advanced Transportation

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Pavement cracking is a significant symptom of pavement deterioration and deficiency. Conventional manual inspections of road condition are gradually replaced by novel automated inspection systems. As a result, a great amount of pavement surface information is digitized by these systems with a high resolution. With pavement surface data, pavement cracks can be detected using crack detection algorithms. In this paper, a fully automated algorithm for segmenting and enhancing pavement crack is proposed, which consists of four major procedures. First, a preprocessing procedure is employed to remove spurious noise and rectify the original 3D pavement data. Second, crack saliency maps are segmented from 3D pavement data using steerable matched filter bank. Third, 2D tensor voting is applied to crack saliency maps to achieve better curve continuity of crack structure and higher accuracy. Finally, postprocessing procedures are used to remove redundant noises. The proposed procedures were evaluated over 200 asphalt pavement images with diverse cracks. The experimental results demonstrated that the proposed method showed a high performance and could achieve average precision of 88.38%, recall of 93.15%, and F-measure of 90.68%, respectively. Accordingly, the proposed approach can be helpful in automated pavement condition assessment.

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Section: Experimental Results and Comparisonmentioning

confidence: 99%

“…(3) The depth information collected by 3D techniques is more helpful in analyzing cracks, textures, rutting, etc. [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Automatic Segmentation and Enhancement of Pavement Cracks Based on 3D Pavement Images

Wang

Zhang

et al. 2019

Journal of Advanced Transportation

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“…The longitudinal resolution, which depends on vehicle speed at which the road is imaged, is reported as 3-6 cm [11]. Li et al [12] also developed a laser triangulationbased mobile system with at horizontal and vertical resolutions of around 2 mm. Measurements made on rutting are compared against an independent measurement procedure resulting in a good agreement.…”

Section: Imaging Pavements In 3d: State Of the Artmentioning

confidence: 99%

“…Measurements made on rutting are compared against an independent measurement procedure resulting in a good agreement. 3D geometric features, obtained from the scanned data, are used to differentiate different distresses, like potholes and rutting [12].…”

Section: Imaging Pavements In 3d: State Of the Artmentioning

confidence: 99%

A Review of Three-Dimensional Imaging Technologies for Pavement Distress Detection and Measurements

Mathavan

Kamal

Rahman

2015

IEEE Trans. Intell. Transport. Syst.

131

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Abstract-With the ever-increasing emphasis on maintaining road assets to a high standard, the need for fast accurate inspection for road distresses is becoming extremely important. Surface distresses on roads are essentially three dimensional (3-D) in nature. Automated visual surveys are the best option available. However, the imaging conditions, in terms of lighting, etc., are very random. For example, the challenge of measuring the volume of the pothole requires a large field of view with a reasonable spatial resolution, whereas microtexture evaluation requires very accurate imaging. Within the two extremes, there is a range of situations that require 3-D imaging. Three-dimensional imaging consists of a number of techniques such as interferometry and depth from focus. Out of these, laser imagers are mainly used for road surface distress inspection. Many other techniques are relatively unknown among the transportation community, and industrial products are rare. The main impetus for this paper is derived from the rarity of 3-D industrial imagers that employ alternative techniques for use in transportation. In addition, the need for this work is also highlighted by a lack of literature that evaluates the relative merits/ demerits of various imaging methods for different distress measurement situations in relation to pavements. This overview will create awareness of available 3-D imaging methods in order to help make a fast initial technology selection and deployment. The review is expected to be helpful for researchers, practicing engineers, and decision makers in transportation engineering.

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“…With the development of measurement sensors, various technological solutions have been applied to automatically detect and report pavement distress and performance indicators information for government agencies in order to accelerate pavement maintenance and management tasks. Typical asphalt pavement distresses—e.g., cracks [ 1 , 2 , 3 ], potholes [ 4 , 5 ], rutting [ 5 , 6 ]—often cause significant safety and economic problems [ 1 , 7 , 8 , 9 ], while some pavement performance indicators including road markings [ 10 ], textures [ 11 , 12 ], and grooves [ 13 , 14 ] have great significance for traffic safety. Moreover, pavement distress and performance indicators are important foundations of intelligent transport systems [ 14 , 15 , 16 ].…”

Section: Introductionmentioning

confidence: 99%

A Component Decomposition Model for 3D Laser Scanning Pavement Data Based on High-Pass Filtering and Sparse Analysis

Gui

Zhang

et al. 2018

Sensors

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High-precision 3D laser scanning pavement data contains rich pavement scene information and certain components associations. Moreover, for pavement maintenance and management, there is an urgent need to develop automatic methods that can extract comprehensive information about different pavement indicators simultaneously. By analyzing the frequency and sparse characteristics of pavement distresses and performance indicators—including the cracks, road markings, rutting, potholes, textures—this paper proposes 3D pavement components decomposition model (3D-PCDM) which decomposes the 3D pavement profiles into sparse components x, low-frequency components f, and vibration components t. Designed high-pass filter was first employed to separate f, then, x and t are separated by total variation de-noising which based on sparse characteristics. Decomposed x can be used to characterize the location and depth information of sparse and sparse derived signals such as cracks, road marks, grooves, and potholes in profiles. Decomposed f can be used to determine the slow deformation of pavement. While decomposed t reflects the fluctuation of the pavement material particles. Experiments were conducted using actual pavement 3D data, the decomposed components can obtain by 3D-PCDM. The effectiveness and accuracy of the x are verified by actual cracks and road markings, the accuracy of extracted sparse components is over 92.75%.

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A real-time 3D scanning system for pavement rutting and pothole detections

Cited by 5 publications

References 4 publications

Automatic Segmentation and Enhancement of Pavement Cracks Based on 3D Pavement Images

Automatic Segmentation and Enhancement of Pavement Cracks Based on 3D Pavement Images

A Review of Three-Dimensional Imaging Technologies for Pavement Distress Detection and Measurements

A Component Decomposition Model for 3D Laser Scanning Pavement Data Based on High-Pass Filtering and Sparse Analysis

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