Aerial Photograph-Based Pavement Surface Distress Detection and Evaluation

Transcription

1 Aerial Photograph-Based Pavement Surface Distress Detection and Evaluation César Hernández Gracia Antonio Sahuquillo Piñero AF2913-Road Construction and Maintenance

2 Table of Contents 1. Introduction 2. Background 3. Study Area 3.1. Manual, Visual Evaluation 3.2. Aerial Photos used 4. Methodology 4.1. Image Processing 4.2. Linear Regression Model Development 4.3. Cross-Validation of Regression Model 5. Results and Discussion 6. Conclusion

3 1. Introduction Purpose: new approach for pavement surface distress detection (Aerial Photos) Objective: prove the viability of this technique as a cost-effective, efficient and safer alternative Organizations involved: New Mexico Department of Transportation (NMDOT) University of New Mexico (UNM) New Mexico State University (NMSU)

4 1. Introduction Pavement Management Program Self-performed (NMDOT). Since 2006 (UNM, NMSU) Manual, visual pavement distress evaluation Data collection sites (test section): 530 feet long intervals from milepost Distress Analysis Results: Distress rate (DR) and Pavement Serviceability Index (PSI) Problems: labor-intensive, time consuming, potentially dangerous Analysis of Aerial Photograph method Overall pavement distress detection Types/spatial resolution of the aerial photos

5 2. Background Methods used to collect distress data: Manual surveys based on visual inspection Automated systems (photography, video, sensors) Past and Current Automated Methods Focused on the detection of cracks (overall level pav. Distress) Limited and non-successful studies based on aerial photos Recent Advances in Remote Sensing High resolution and multiple spectral aerial photos Natural Color / Color-infrared Detailed ground information and large area

6 3. Study Area 6 counties in Northern New Mexico (EEUU) 24 quality assurance/quality control (QA/QC) sites 4 discarded not totally visible or not available at GIS (Geographic Information System) Final sample: 20 study sites totally covered by the aerial photos Data collected Manual, visual pavement evaluation from 2009 and 2011 GIS Database from UNM s Earth Data Analysis Center (EDAC): 2010 and 2011 photos

7 3.1. Manual,Visual Evaluation 8 types of distresses evaluation for flexible pavements: 1. Raveling & Weathering (3) 5. Transverse Cracking (12) 2. Bleeding (2) 6. Alligator Cracking (25) 3. Rutting and Shoving (14) 7. Edge Cracking (3) 4. Longitudinal Cracking (12) 8. Patching (2) Evaluated on a severity and extent scale 0-3 (N/A; Low; Medium; High) Calculation of the Overall Distress Rate:

8 Calculated Distress Rates The lower the DR value, the better the pavement condition

9 3.2 Aerial Photos from EDAC 2010 ortho-corrected natural color aerial photos Spatial resolution: 6 inches 3 spectral bands: blue, green and red Used for 2009 distress data (Assumption: no changes in pavement from 2009 to 2010) 2011 ortho-corrected color-infrared aerial photos Spatial resolution: 1 meter 4 spectral bands: blue, green, red and near-infrared Used for 2011 distress data

10 4. Methodology 4.1. Image Processing Image Mosaic Evaluation Polygon Creation Band Extraction Band Statistics Extraction Linear Regression Model Development Cross-Validation of Regression Model

11 Image Mosaic Evaluation of each site Buffer of 530 feet around the milepost Single photo to cover the whole zone Possible problem: 2 or more photos needed overlapped areas Only 2 milepost needed more than 1 photo (2010 set)

12 Evaluation Polygon Creation Polygon Creation Simulation of the study sites using ArcMap Reference image: corrected aerial photos Output: accurate representation of Earth s surface Elimination of annoying features (pavement marks, overpasses, vehicles, etc.) Data collected (Manual Evaluation rules:nmdot) 2 lanes-highway: positive direction of both lanes Multi-lane Highway: positive and negative direction Milepost +530 feet (to the north towards the top) Milepost -530 feet (to the south towards the bottom) Always the far right driving lane / not turning or passing lanes

13 Band Extraction Obtain different views of the same picture using spectral bands Different distress conditions have different responses for each band Aereal photo must be analyzed band by band

14 4.1.4.Band Statistics Extraction Spacial Analyst tool implemented in ArcMap Each cell of the polygon has its own value of the statistics This value is not fix in time. Depends on the conditions Statistics tables are created, merged together and exported to Excel

15 4. Methodology 4.1. Image Processing Image Mosaic Evaluation Polygon Creation Band Extraction Band Statistics Extraction Linear Regression Model Development Cross-Validation of Regression Model

16 4.2. Linear Regression Analysis Dependent variable: Distress Ratio Performed for 2010 and 2011 data Explanatory variables: Mean, median, standard deviation and variety Based on the experiences of the authors The software provides more statistics

17 4.2. Linear Regression Analysis As much group of explanatory variables as bands

18 4. Methodology 4.1. Image Processing Image Mosaic Evaluation Polygon Creation Band Extraction Band Statistics Extraction Linear Regression Model Development Cross-Validation of Regression Model

19 4.3. Cross Validation of the Regression Model What s that? 6 randomly selected milepost were employed Only for 2009 data, in 2011 there was no pavement evaluation Results of the regression presented in the next point

20 5. Results and Discussion (2009) R-square value=0,8426 (significant at the 0.1 level) Some variables excluded

21 Cross-Validation Mileposts (2009) Statistically not different Problems in the 6th milepost. (Quick deterioration between 2009 and 2010)

22 Results and Discussion (2011) R-square value=0,4753 without infrared band R-square value=0,7085 with infrared band No cross validation

23 6. Conclusions Save in time and money Results are more accurate for 4 bands and for 6 inches of spacial resolution but they belong to different experiments Bigger scope to compare results This experiment could lead to an automatic process of distress evaluation