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James Tsai, Ph.D., P.E., Associate Professor (Presenter) Co-PI: Zhaohua Wang, Ph.D., Senior Research Engineer
Feng Li, Ph.D. Candidate Chengbo Ai, Ph.D. Candidate
Georgia Institute of Technology
April 17, 2012
A Sensor-Based and Spatially Enabled Roadway Asset Management System
9th National Conference on Transportation Asset Management Making Asset Management Work in Your Organization
(A Reliable, Cost-effective Performance Measurement Technology)
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Acknowledgements
The study presented was sponsored by US DOT RITA program and GDOT.
The views, opinions, findings and conclusions
reflected in this presentation are the responsibility of the authors only and do not represent the official policy or position of the USDOT, RITA, or any State or other entity.
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Outline • Introduction
– Architecture of A Sensor-Based and Spatially Enabled Roadway Asset Management System
– Research objective – Research focuses
• Georgia Tech Sensing Vehicle • Pavement rutting/crack • Traffic sign • Summary
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A Sensor-Based and Spatially Enabled Roadway Asset Management System
Phase 2 Phase 1
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Research Objective
• To develop and validate an innovative and cost-effective means to inventory roadway assets and evaluate their condition (e.g. asphalt pavement surface conditions and traffic signs).
A Reliable and Cost-effective
Measurement Technology
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Research Roadmap and Focuses in Phase 1
Integration of Sensing Devices
Data Collection
Application Validation (Seven Focuses)
•LCMS (for pavement asset) • Imaging and mobile LiDAR (for traffic sign asset)
•Agencies’ data • Sensing device collected data
Outreach Plan
1. Network-level rutting measurement 2. Localized rutting identification 3. Performance evaluation of automatic
crack detection algorithms 4. Validation of crack detection using
3D continuous transverse profiles 5. Develop and validate an enhanced
sign inventory procedure using mobile-LiDAR and image processing technology
6. Validate the sign condition assessment using mobile LiDAR
7. Validate an integrated GIS-based sign asset management system
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Georgia Tech Sensing Vehicle (All-In-One Technology)
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3D Line Laser Imaging Technology
1. Transverse dir : 1 mm 2. Elevation: 0.5 mm 3. Data points collected per second and width covered: 2 (lasers) * 2048 (points/profile/laser) * 5600 HZ = 22,937,600 points 2 (lasers) * 2048 (points/profile/laser) * 1 (mm) = 4.096 m
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High resolution LiDAR
LiDAR and Imaging System
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1. Rutting • Rut depth measurement • Localized rutting
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Point-based Rut Bar
Road Profile
Left Wheelpath Right Wheelpath
Rut Depth
(McGhee 2004)
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Rut Depth Measurement
0 200 400 600 800 1000 1200 1400 1600 1800-20
-18
-16
-14
-12
-10
-8
-6
-4
-2
0
Transverse Direction /mm
Dep
th /m
m
Raw ProfileSmoothed Profile
1.8m Straight-Edge
Road Profile
Wheelpath
Rut Depth
90°
2. Field Test
1. Lab Test
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Accuracy of Rut Depth Measurement (1) Note: 1. Lab Test (Absolute error less than 1mm)
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Accuracy of Rut Depth Measurement (2) Note: 2. Field Test (Absolute error about 2mm)
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Assessment of Rut Bar System Errors
0 10 20 30 40 50 60 70 80 90 100 1100
20
40
60
80
100
120
140
160
Relative Error
Freq
uenc
y
3-point rut barNormal Fit5-point rut barNormal Fit9-point rut barNormal Fit39-point rut barNormal Fit
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Isolated Rut on I-95
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Isolated Rut Identification and Measurement 480 ft
MP: 100.16 to 100.17 Length = 480ft
Max Depth = ¾ in. Area = 2600 ft2 Volume = 51 ft3
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Cracking • Automatic crack detection • Performance evaluation
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Problem on Region-based Performance Measurement
Algorithm 1 result(3ft), 100% detection
Algorithm 2 result (3ft), 100% detection
Ground truth(3ft)
5ft by 5ft pavement area sample
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Linear Buffered Hausdorff Quantification Method
)),(),,(max(),( ABhBAhBABH =
∑∈
∈−=
Aa BbLbasat
mBAh ||||min1),(
L
),(100)Measure(SM Scoring BABH−=
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A Buffered Hausdorff Distance Scoring Method
(a) Original Image
(b) Ground Truth Image
(c) Dynamic Optimization Result Score: 92
(d) Canny Edge Detection Result Score: 14
(e) Crack Seed Verification Result Score: 3
(f) Iterated Clipping Result Score: 64
(a)
(c) (d) (e)
(a)
(f)
(b)
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Advantage of 3D data over 2D data on crack detection
2D data 3D data
With 3D continuous profile technology, it is a lot more clear to distinguish a crack from the surrounding pavements
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Laboratory Test for Crack Detection
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Field Test for Crack Detection (1)
Daytime (no shadow) Shadow Night
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Field Test for Crack Detection (2)
Daytime (score = 98.3) Night (score = 98.0)
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Crack Width Measurement (1)
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Demo Video
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Validation of Pavement Condition Assessment Using 3D Line Laser Imaging Technology
(on-going tasks)
• Asphalt pavement crack classification • Concrete pavement condition evaluation (faulting,
spalling, crack, should joint drop, etc.)
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3. Traffic Sign Inventory • Using mobile LiDAR and image processing
algorithms • Sign retro-reflectivity condition assessment
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Image-based Traffic Sign Detection
• MUTCD Shapes: circle, triangle, rectangle, pentagon, Octagon, etc.
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(c) Extracted speed limit digits
(b) Processed binary image after color segmentation
(a) Raw image containing speed limit sign
Sign Recognition Using Image Pattern Recognition Algorithms
Incorporated other features, Harr features derived from the Adaboost Cascade algorithm, used effectively in face recognition
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Sign Pattern Recognition
(NCHRP IDEA Final Project Report, Tsai, 2009)
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Sign Detection Demo
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Sign Condition Change Detection
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Sign Change Detection
Scale-invariant Feature Transform (SIFT)
2005
2005
2003 2003
2003
Courtesy of LADOTD for providing testing images.
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Sign Change Detection (cont.)
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Use of Mobile LiDAR for Sign Detection
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Summary • It is promising to use emerging sensor technology to develop a
cost-effective measurement technology. • 3D line laser imaging technology is capable of building a “All
Purpose” device for assessing pavement surface conditions: rutting, cracking, potholes, macro-texture, etc.
• The accuracy and repeatibility of rut depth measurement can be improved using 3D line laser technology. It can be applied for network-level rutting survey and isolated rut identification.
• The accuracy of crack detection and width measurement can be improved using 3D line laser imaging technology.
• It can be further applied to crack classification and concrete condition assessment (e.g. faulting, spalling, broken slabs).
• Mobile LiDAR and image processing algorithms can be used to improve the efficiency of sign data collection under a well-designed sign inventory procedure.
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Outreach Plan
• Work with GDOT to initiate pilot studies to extend the research results to practical application: I-285 interstate highway pavement condition evaluation to demonstrate the practical use of the technology and how to generate the information, including report that can support pavement maintenance operation and decision-making.
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Thanks