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Pavement Surface Defects: Classification and Quantification over a Road Network

Pavement Surface Defects: Classification and Quantification over a Road Network. Alejandro Amírola Sanz AEPO, S.A. (Spain) Equipment Research and Development Department Infrastructure Management Division aamirola@aepo.es. 1.Image Acquisition. 3.Quantification. Indexes Calculation.

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Pavement Surface Defects: Classification and Quantification over a Road Network

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  1. Pavement Surface Defects: Classification and Quantification over a Road Network • Alejandro Amírola Sanz • AEPO, S.A. (Spain) • Equipment Research and Development Department • Infrastructure Management Division • aamirola@aepo.es

  2. 1.Image Acquisition 3.Quantification. Indexes Calculation Numerical Results 1. Scope of the Analysis Data shown in figures only as sample of outputs 4.Graphics and Maps Generation 2.Surface Defects Identification Road Network managed by the Spanish DGC: ~30.000 Km

  3. 60 pixels = 60 mm 60 pixels = 60 mm 600 pixels = 600 mm 400 pixels = 400 mm 50 pixels = 50 mm 50 pixels = 50 mm YES. Is a Crack Is it a Crack? 2. What is a crack? 1. Is needed a detailed definition of crack for develop useful Distresses Indicators? 2. Can we assess the human pattern recognition accuracy? 3. How can we evaluate the automatic detection systems accuracy? “A crack is a discontinuity in the pavement surface with minimum dimensions of 1-mm width and 25-mm length.”(AASHTO PP44-01) “A crack is a discontinuity in the road surface that has a minimum length, width and depth.”(PIARC Technical Committee C4.2 Road/Vehicle Interaction) Sample image:

  4. 3. Detection Methods. Automatic Systems & Human Reviewers Lane width: 4m = 4000 pixels Image length: 1m = 1000 pixels

  5. 4. Quantification Methods. Longitudinal & Surface • Length of crack related to section length • i.e.: 10.6 m of crack in 1 meter lane length. • (Severe Cracking Level) B) Portion of Surface cracked related to total surface Grid size: 10 cm x 10 cm 1m length = 400 blocks i.e.: 108 blocks cracked in 1 meter length. 27% cracked surface Each 10cmx10cm block is equivalent to 10 cm of crack length. Conversion from longitudinal to surface reference can be done

  6. 5.a Classification & Quantification Cracks / Distresses: 1.Longitudinal Cracking: Longitudinal Cracks Index (LCI) (IFL): 2.Transversal Cracking: Cracks Length (CL) (LF): Transversal Cracks Index (TCI) (IFT): 3.Alligator Cracking: Alligator Cracks Index (ACI) (IFM):

  7. 5.a Classification & Quantification Cracks / Distresses: Cracks Length (CL) (LF): Sample results (crack meter / meter section leght) 0.82 0 16.7 4.36

  8. CEI CL (m crack/m section) 5.a Classification & Quantification Cracks / Distresses: Cracks Length (CL) (LF): Cracks Equivalent Index (CEI) Conversion Range: CL CEI Severe Medium Low Very Severe

  9. 5.a Classification & Quantification Cracks / Distresses: Cracks Length (CL) (LF): Sample results (CEI) 0.82 MEDIUM 0 LOW 1.6 SEVERE 2.2 VERY SEVERE

  10. 5.b Classification & Quantification Other Indexes: Reparation Index: Sealing Index: Peeling Index:

  11. C A B D E Longitudinal Transversal Alligator Total 6. Division of the results considering wheel paths Total

  12. 7. Accuracy levels Test Road Section: Heterogeneous 10 km section Consider the road section surface divided by a 10cmx10cm grid. Measure the distresses by various trained human reviewers. Human Results: Reference Results: - 12,8% of the blocks are crack block - 87,2% of the blocks are non crack block • Also obtained 1 reference value per each block over all the control section The difference between average and each individual reviewer is not good enough for controlling the accuracy. i. e.: One system that provide the same values as the reference, but detects the distresses on different locations is a bad detection system.

  13. “False Positives” “Missing Crack” 7. Accuracy levels Test Road Section: Heterogeneous 10 km section Comparison of each Human Reviewer vs. Reference Results: Comparison of the deviations block by block Reviewer Result Non Crack Crack Crack Non Crack Target

  14. 7. Accuracy levels Target Test Road Section: Heterogeneous 10 km section Comparison of each Human Reviewer vs. Reference Results: Missing Crack: around 1-2.5% Non Crack Crack Reviewer 1 Non Crack Crack Reviewer 2 False Positives: around 1-2% Crack Non Crack Crack Non Crack Target Target These values must be accepted as long as we accept that “the human pattern recognition is quite ambiguous” (PIARC Technical Committee C4.2 Road/Vehicle Interaction Conclusions on Evaluating the Performance of Automated Pavement Cracking Measurement Equipment) Non Crack Crack Reviewer 3 Non Crack Crack Reviewer 4 Crack Non Crack Crack Non Crack Target Target

  15. 7. Accuracy levels Test Road Section: Heterogeneous 10 km section Automatic Systems vs. Reference Results: Missing Crack: around 1-2.5% Estimated Accuracy level obtained by human reviewers False Positives: around 1-2% At the moment, we are using automatic detection system that have: ~3% Missing Crack ~6% False Positives Is reasonable expect to develop automatic systems that can recognize distresses better than human reviewers? Can the automatic detection systems get better accuracy levels that humans?

  16. 8. Conclusions • A detailed definition of what is a crack is not a critical issue for the study and characterization of pavement distresses at the Network Level. • Definition of indexes is critical to obtain good and useful results. These indexes can be customized for the end user (Road Administrators) • Human accuracy levels should be considered as a reference when automatic detection systems are being developed.

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