Pavement drainage evaluation system

• US 10,197,486 B2
• Filed: 03/21/2018
• Issued: 02/05/2019
• Est. Priority Date: 04/30/2017
• Status: Active Grant

• Current Assignee: Amirkabir University of Technology, Behrouz Mataei, Fereidoon Moghadas Nejad, Hamzeh Zakeri
• Original Assignee: Amirkabir University of Technology, Behrouz Mataei, Fereidoon Moghadas Nejad, Hamzeh Zakeri
• Inventors: Mataei, Behrouz, Nejad, Fereidoon Moghadas, Zakeri, Hamzeh
• Primary Examiner(s): Punnoose, Roy M
• Application Number:
  US15/927,627
• Publication Number:
  US 20180284004A1
• Time in Patent Office: 321 Days
• Field of Search: 356336
• CPC Class Codes: E01C 23/01 Devices or auxiliary means for setting-out or checking the configuration of new surfacing, e.g. templates, screed or reference line supports form rails E01C19/50; Applications of apparatus for measuring, indicating, or recording the surface configuration of existing surfacing, e.g. profilographs E01C23/07 takes precedence; measuring roughness or irregularity in general G01B
• CPC Class Codes: G01N 15/0211 Investigating a scatter or diffraction pattern
• CPC Class Codes: G01N 15/0227 using imaging; using holography
• CPC Class Codes: G01N 15/082 Investigating permeability by forcing a fluid through a sample
• CPC Class Codes: G01N 2015/0846 by use of radiation, e.g. transmitted or reflected light
• CPC Class Codes: G01N 2021/8887 based on image processing techniques
• CPC Class Codes: G01N 2021/95615 with stored comparision signal
• CPC Class Codes: G01N 21/532 with measurement of scattering and transmission
• CPC Class Codes: G01N 21/8851 Scan or image signal processing specially adapted therefor, e.g. for scan signal adjustment, for detecting different kinds of defects, for compensating for structures, markings, edges G01N21/8806 and G01N21/93 - G01N21/95692 take precedence; optical measurement of dimensions G01B11/00; optical scanning G02B26/10; image transformation G06T3/00; computerised image enhancement G06T5/00; image processing per se for flaw detection G06T7/0002
• CPC Class Codes: G01N 21/95607 using a comparative method
• CPC Class Codes: G06T 2207/10016 Video; Image sequence
• CPC Class Codes: G06T 2207/10024 Color image
• CPC Class Codes: G06T 2207/30132 Masonry; Concrete
• CPC Class Codes: G06T 7/0004 Industrial image inspection
• CPC Class Codes: G06T 7/0006 using a design-rule based approach

Abstract

A system for evaluating drainage condition of a pavement surface is disclosed. The system may include: a spray assembly configured to spray a liquid over the pavement surface, an image capturing device configured to capture consecutive images of the pavement surface at predetermined intervals, and an image processing unit coupled with the image capturing device. The image processing unit may include: a processor, and a memory that may be configured to store executable instructions to cause the processor to process the captured images to evaluate the drainage condition of the pavement by a number of steps that may include: converting each captured image into a binary image, the binary image including a first plurality of pixels representing pavement aggregates protruding from the sprayed liquid and a second plurality of pixels representing the sprayed liquid; applying optical granulometry to the binary images to obtain a rate of change for protruding pavement aggregates count and a rate of change for area of protruding pavement aggregates across the binary images; and classifying the pavement surface according to a drainage condition type based at least in part on a combination of the rate of change for the protruding pavement aggregates count and the rate of change for the area of protruding pavement aggregates.

Claims

1. A system for evaluating drainage condition of a pavement surface, the system comprising:

a spray assembly configured to spray a liquid over the pavement surface;

an image capturing device configured to capture consecutive images of the pavement surface; and

an image processing unit coupled with the image capturing device, the image processing unit including;

a processor; and

a memory configured to store executable instructions to cause the processor to process the captured consecutive images to evaluate the drainage condition of the pavement surface, the operations including;

converting each of the captured consecutive images into a binary image, the binary image including a first plurality of pixels representing pavement aggregates protruding from the sprayed liquid and a second plurality of pixels representing the sprayed liquid;

applying optical granulometry to the binary images to obtain a rate of change for protruding pavement aggregates count and a rate of change for area of protruding pavement aggregates across the binary images; and

identifying the drainage condition, from among a given plurality of drainage conditions, based at least in part on a combination of the rate of change for the protruding pavement aggregates count and the rate of change for the area of protruding pavement aggregates.

2. The system of claim 1, wherein:

the given plurality of drainage conditions includes a given plurality of drainage condition types,

the given plurality of drainage condition types includes a good drainage condition and a bad drainage condition, and

identifying the drainage condition includes classifying the drainage condition into a drainage condition types among the given plurality of drainage condition types.

3. The system of claim 1, wherein:

the image capturing device is configured to capture consecutive images of the pavement surface at predetermined intervals, and

applying optical granulometry to the binary images is configured wherein the rate of change for protruding pavement aggregates count and the rate of change for area of protruding pavement are each based, at least in part, on the predetermined intervals.

4. The system of claim 1, wherein:

captured consecutive images include a captured first image and a captured second image, wherein respective capture times of the captured first image and the captured first image are spaced apart by a time difference,

converting each of the captured consecutive image into a binary image includes converting the captured first image into a first binary image and converting the captured second image into a second binary image, and

applying optical granulometry to the binary images is configured wherein the rate of change for protruding pavement aggregates count and the rate of change for area of protruding pavement aggregates across the binary images are each based, at least in part, on the time difference.

5. The system according to claim 1, wherein the liquid includes a colored liquid, the colored liquid being of a color such that a contrast exists between a color of the pavement surface and the color of the colored liquid.

6. The system according to claim 1, further comprising a lighting assembly, the lighting assembly being configured to project light toward the pavement surface.

7. The system according to claim 1, wherein the image capturing device includes:

a camera, the camera being configured to capture a series of the consecutive images from the pavement surface at predetermined intervals; and

a camera height adjustment mechanism coupled with the camera configured to linearly move the camera along an axis perpendicular to the pavement surface.

8. The system according to claim 1, further comprising an enclosure configured to enclose the pavement surface from ambient light.

9. The system according to claim 1, wherein the spray assembly includes at least two nozzles, and at least one of the at least two nozzles is positioned with a predetermined angle relative to the pavement surface.

10. A method for evaluating drainage condition of a pavement surface, the method comprising:

spraying a liquid over a target pavement surface;

capturing consecutive images of the target pavement surface;

converting each captured image into a binary image, the binary image including a first plurality of pixels representing pavement aggregates protruding from the colored liquid and a second plurality of pixels representing the colored liquid;

applying optical granulometry to consecutive binary images to obtain a rate of change for protruding pavement aggregates count and a rate of change for area of protruding pavement aggregates across the consecutive binary images; and

identifying the drainage condition of the pavement surface, from among a given plurality of drainage conditions, based at least in part on a combination of the rate of change for the protruding pavement aggregates count and the rate of change for the area of protruding pavement aggregates.

11. The method of claim 10, wherein:

capturing consecutive images of the pavement surface includes capturing consecutive images of the pavement surface at predetermined intervals, and

applying optical granulometry to the binary images is configured wherein the rate of change for protruding pavement aggregates count and the rate of change for area of protruding pavement are each based, at least in part, on the predetermined intervals.

12. The method of claim 10, wherein:

capturing consecutive images include capturing a first image and capturing second image, wherein respective capture times of the captured first image and the captured first image are spaced apart by a time difference,

converting each of the captured consecutive image into a binary image includes converting the captured first image into a first binary image and converting the captured second image into a second binary image, and

applying optical granulometry to the binary images is configured wherein the rate of change for protruding pavement aggregates count and the rate of change for area of protruding pavement aggregates across the binary images are each based, at least in part, on the time difference.

13. The method according to claim 10, wherein spraying a colored liquid over the pavement surface includes spraying a colored liquid with a color that has a contrast from the color of the target pavement surface.

14. The method according to claim 10, wherein converting each captured image into the binary image includes applying a thresholding method to each captured image.

15. The method according to claim 10, wherein converting each captured image into a binary image includes converting each captured image into a binary image, the binary image including a first plurality of pixels with a first intensity of zero representing pavement aggregates protruding from the colored liquid and a second plurality of pixels with a second intensity of 1 representing the colored liquid.

16. The method according to claim 10, wherein applying optical granulometry to consecutive binary images further includes obtaining a rate of change for the protruding pavement aggregates surface distribution by dividing the rate of change for protruding pavement aggregates count by the rate of change for the area of protruding pavement aggregates by the predetermined interval.

17. The method of claim 16, wherein:

the given plurality of drainage conditions includes a given plurality of drainage condition types,

the given plurality of drainage condition types includes a good drainage condition and a bad drainage condition, and

identifying the drainage condition includes classifying the drainage condition into a drainage condition types among the given plurality of drainage condition types.

18. The method according to claim 17, wherein classifying the pavement surface according to a drainage condition type includes comparing the calculated rate of change for the protruding pavement aggregates surface distribution to a variation range for the rate of change for the protruding pavement aggregates surface distribution.

19. The method according to claim 17, wherein classifying the pavement surface according to a drainage condition type includes comparing the calculated rate of change for protruding pavement aggregates count to a variation range for the rate of change for protruding pavement aggregates count.

20. The method according to claim 17, wherein classifying the pavement surface according to a drainage condition type includes comparing the calculated rate of change for the area of protruding pavement aggregates to a variation range for the rate of change for the area of protruding pavement aggregates.