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

2. Portorož, Slovenia Index 1.Scope of the Analysis. 2.What is a crack? 3.Detection Methods. Automatic Systems & Human Reviewers. 4.Quantification methods. Longitudinal & Surface. 5.Classification and Quantification. 6.Division of results considering wheel paths. 7.Accuracy Levels.

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

4. Portorož, Slovenia 2. What is a crack? “A crack is a discontinuity in the pavement surface with minimum dimensions of 1-mm width and 25-mm length.” (AASHTO PP44-01) 600 pixels = 600 mm 400 pixels = 400 mm 50 pixels = 50 mm 60 pixels = 60 mm YES. Is a Crack Sample image: 50 pixels = 50 mm 60 pixels = 60 mm Is it 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 road surface that has a minimum length, width and depth.” (PIARC Technical Committee C4.2 Road/Vehicle Interaction)

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

6. Portorož, Slovenia 4. Quantification Methods. Longitudinal & Surface A)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

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

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

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

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

11. Portorož, Slovenia 5.b Classification & Quantification Other Indexes:  Sealing Index:  Peeling Index:  Reparation Index:

12. Portorož, Slovenia 6. Division of the results considering wheel paths A B C D E LCILCI E LCI D LCI C LCI B LCI A TCITCI E TCI D TCI C TCI B TCI A ACIACI E ACI D ACI C ACI B ACI A CLCL E CL D CL C CL B CL A Other indexes (sealing,…) Total Alligator Longitudinal Transversal Total

13. Portorož, Slovenia 7. Accuracy levels Consider the road section surface divided by a 10cmx10cm grid. Reference Results: - 12,8% of the blocks are crack block - 87,2% of the blocks are non crack block Test Road Section: Heterogeneous 10 km section Measure the distresses by various trained human reviewers. Human Results: Reviewer1234Average Crack 14.5%13.5%10.9%12.1%12.8% Non Crack 85.4%86.5%89.0%87.8%87.2% - 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.

14. Portorož, Slovenia 7. Accuracy levels Comparison of each Human Reviewer vs. Reference Results: Comparison of the deviations block by block Test Road Section: Heterogeneous 10 km section Target Reviewer Result Non Crack Crack Crack Non Crack Real Crack marked as Crack% Real Non Crack marked as Crack% Real Crack marked as Non Crack% Real Non Crack marked as Non Crack% “False Positives” “Missing Crack”

15. Portorož, Slovenia 7. Accuracy levels Comparison of each Human Reviewer vs. Reference Results: Test Road Section: Heterogeneous 10 km section Target Reviewer 1 11.862.68 1.1584.31 12.8% Crack 0% 87.2% non crack Crack Non Crack Non Crack Crack Target Reviewer 2 11.551.95 1.4685.04 Crack Non Crack Non Crack Crack Target Reviewer 3 10.560.43 2.4586.56 Crack Non Crack Non Crack Crack Target Reviewer 4 11.340.85 1.6786.14 Crack Non Crack Non Crack Crack Missing Crack: around 1-2.5% False Positives: around 1-2% 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) Target

16. Portorož, Slovenia 7. Accuracy levels Test Road Section: Heterogeneous 10 km section Automatic Systems vs. Reference Results: Missing Crack: around 1-2.5% False Positives: around 1-2% Estimated Accuracy level obtained by human reviewers 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?

17. Portorož, Slovenia 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.