1、Standard Practice for Collecting Images of Pavement Surfaces for Distress Detection AASHTO Designation: R 86-181Technical Section: 5a, Pavement Measurement Release: Group 1 (April) American Association of State Highway and Transportation Officials 444 North Capitol Street N.W., Suite 249 Washington,
2、 D.C. 20001 TS-5a R 86-1 AASHTO Standard Practice for Collecting Images of Pavement Surfaces for Distress Detection AASHTO Designation: R 86-181Technical Section: 5a, Pavement Measurement Release: Group 1 (April) 1. SCOPE 1.1. This practice outlines the procedures for collecting images of pavement s
3、urfaces utilizing automated methods for the purpose of distress detection for both network- and project-level analysis. Detailed specifications are not included for equipment or instruments used to collect the images. According to this standard, any equipment that can be adequately validated to meet
4、 the functionality stipulated herein is considered acceptable. The goal is to achieve a significant level of standardization that will contribute to the production of consistent pavement condition estimates while not unduly limiting innovation. 1.2. The images are to be collected utilizing a platfor
5、m traveling at or near the prevailing highway speed. 1.3. The data collected should cover the entire driven lane in the travel direction. 1.4. This practice does not purport to address all of the safety concerns, if any, associated with its use. It is the responsibility of the user of this standard
6、to establish appropriate safety and health practices and determine the applicability of regulatory limitations related to and prior to its use. 2. TERMINOLOGY 2.1. Definitions: 2.1.1. cracka fissure of the pavement material at the surface with minimum dimensions of 1 mm (0.04 in.) width and 25 mm (1
7、 in.) length. 2.1.2. crack widththe average gap in millimeters (inches) between the two edges of a crack measured at points along the gap with a minimum spacing between the measurement points of 3 mm (0.12 in.). 2.1.3. pavement distressexternal indications of pavement defects or deterioration. 2.1.4
8、. pavement imagea representation of the pavement that describes a characteristic (gray scale, color, temperature, elevation, etc.) of a matrix of points (pixels) on the pavement surface. 2018 by the American Association of State Highway and Transportation Officials. All rights reserved. Duplication
9、is a violation of applicable law.TS-5a R 86-2 AASHTO 3. SIGNIFICANCE AND USE 3.1. This practice outlines the procedures for collecting images of pavement surfaces utilizing automated methods for the purpose of distress detection. Its purpose is to produce consistent data collection. 3.2. It is recog
10、nized that the requirements for the collected image(s) listed below are linked to the capability of the associated distress detection system. This linkage is seen as necessary due to the current immaturity of the technology. It is hoped that future developments will provide for a more objective meth
11、od of measuring performance that is independent of the detection system and easier to implement. There are methods to determine certain limited aspects of the collection processes such as the ability to collect images of 1-, 2-, 4-, 7-, and 10-mm (0.04-, 0.08-, 0.16-, 0.28-, and 0.4-in.) diameter ro
12、ds placed on the pavement at various angles and positions. These processes can be particularly helpful in determining whether there have been any major changes in equipment performance. 4. DATA COLLECTION 4.1. General GuidelinesEach agency shall designate the lane(s) and direction(s) of travel to be
13、 surveyed or rated based on sound engineering principles and management needs within the agency. The following guidelines are recommended as minimums to provide a necessary database and for long-term uniformity. 4.2. Survey: 4.2.1. Reported images at least 4.0 m (13 ft) wide. Preferably, the images
14、should be 4.25 m (14 ft) wide to include an additional 300 mm (12 in.) on the shoulder side so that pavement edge distress beyond the marking can be captured. Typically, vehicle wander requires that images at least 300 mm (12 in.) wider than the required image width be collected in order to report f
15、ull-width data. Data beyond the required image width may be discarded. Image length in the travel direction shall be not greater than 100 m (325 ft). 4.2.2. The lanes for which the data are collected will depend on final use. Typically, network data are collected in the outside travel lane, and proj
16、ect-level collection covers all lanes. 4.2.3. For network data collection, it is desirable to collect the data in the same travel direction on each cycle. 4.2.4. Data collection should not be performed in the presence of standing water or other surface contaminants. 4.3. Pavement Image: 4.3.1. The i
17、mages must provide sufficient difference between data point values representing distressed and nondistressed areas that subsequent distress detection techniques can delineate a minimum of 33 percent of all cracks under 3 mm (0.12 in.), 60 percent of all cracks present from 3 mm (0.12 in.) and under
18、5 mm (0.2 in.) wide, and 85 percent of all cracks 5 mm (0.2 in.) wide or wider regardless of orientation or type (see Note 1). The determination of this capability will be made utilizing a minimum of ten 0.03-km (100-ft) samples containing an average of at least five such cracks per sample. 4.3.2. T
19、he images should be sufficiently void of erroneous differences between data point values that a section of pavement without distress, discontinuities, or pavement markings contains less than 3 m (10 ft) total length of detected false cracking in 50 m2(540 ft2) of pavement (see Note 1). The 2018 by t
20、he American Association of State Highway and Transportation Officials. All rights reserved. Duplication is a violation of applicable law.TS-5a R 86-3 AASHTO determination of this capability will be made utilizing a minimum of ten 0.03-km (100-ft) samples of various types that meet the criteria. Note
21、 1These performance values are the estimates of a panel of experts based on current technology. Ongoing research and equipment developments will better define and improve these criteria over the next few years. As capabilities are better defined, separate levels of performance may be established for
22、 two or three classes of equipment. 4.3.3. Detected average crack width for each crack detected in Section 4.3.2 must be within 20 percent or 1 mm (0.04 in.), whichever is larger, of the actual width with at least 85 percent confidence. 4.3.4. Pavement images may be visible or infrared video that is
23、 either illuminated or passive. It may also be a dimensional map, or any combination of technologies that achieves sufficient distress detection reliability stated in Section 4.3. 5. DATA REPORTING 5.1. The location (latitude and longitude) of the first data point on the shoulder side of each image
24、should be reported as a minimum, along with a unique image identifier. 5.2. The image scale should be equal in both longitudinal and transverse directions and the value reported. The scale value of the z-axis of any nonintensity images should also be reported. Other useful comment data that can affe
25、ct image analysis, such as the presence of crack seal, railroad tracks, or excessive pavement marking, should be reported with the image. 6. SYSTEM VALIDATION 6.1. The process of calibrating and checking the performance of the measurement equipment is left to the agency. Generally, the agency should
26、 follow the manufacturers recommendations for calibrating and verifying the performance of the equipment. The following considerations should be included in any program. 6.1.1. Location accuracy: 6.1.1.1. Distance measuring instrument accuracy; 6.1.1.2. Latitudelongitude accuracy. 6.1.2. Crack width
27、 and length in all orientations. 6.1.3. Crack delineation (sensitivity to the characteristic(s) that define(s) a fissure). 6.1.4. Minimum resolution versus delineation level. 6.1.5. Minimum resolution versus crack angle. 6.1.6. System platform stability and environmental impacts (moisture/wind/tempe
28、rature): 6.1.6.1. Performance at various sun angles and intensities; 6.1.6.2. Performance at various speeds; 6.1.6.3. Performance at various vehicle attitudes and distances relative to the pavement; 2018 by the American Association of State Highway and Transportation Officials. All rights reserved.
29、Duplication is a violation of applicable law.TS-5a R 86-4 AASHTO 6.1.6.4. Performance at various humidity and temperature levels; 6.1.6.5. Performance at different wind conditions; 6.1.6.6. Performance under various lighting conditions; and 6.1.6.7. Resolution versus position of cracks due to optica
30、l distortion. 6.2. Ground truth for system calibration and verification shall be the close physical examination of the pavement surface by trained technicians with measurement instruments during a lane closure. 6.3. Validation/Acceptance Report: 6.3.1. The ground truth report is a crack map depictin
31、g each crack in the section and its unique identifier. Included with the map is a table listing the crack identifiers along with the location, length, and average width of each crack in each summary section. 6.3.2. The validation report will tabulate the cracks from the ground truth report into seve
32、rity categories based on average width and present a comparison to those detected by the automated system in each category. It will also present the length of false cracking reported by the automated system. 6.4. The operator and driver (optional) are critical components of the total measurement sys
33、tem. They must be trained in equipment operation, including instrument failure detection and system management. Smooth, precise operation of the instrument platform is necessary for optimum results. 6.5. Quality Control/Quality Assurance (QC/QA): 6.5.1. The formal calibration and performance verific
34、ation program may be supplemented with a validation program in which the equipment traverses defined portion(s) of pavement on a regular basis. The validation site should represent most of the data collection variables that the system is expected to encounter during routine data collection. Results
35、are then compared for reasonableness with previous runs. A typical implementation of this process would involve 5 km (3 mi) of data collection and be performed monthly. 7. KEYWORDS 7.1. Asphalt pavement surface; automated data collection; concrete pavement surface; pavement distress; pavement images
36、; pavement management. 8. REFERENCES 8.1. AASHTO R 85, Quantifying Cracks in Asphalt Pavement Surfaces from Collected Pavement Images Utilizing Automated Methods. 8.2. ASTM E1656/E1656M, Standard Guide for Classification of Automated Pavement Condition Survey Equipment. 8.3. FHWA. Distress Identific
37、ation Manual for the Long-Term Pavement Performance Program, FHWA Report RD-03-031. 1Formerly AASHTO Provisional Standard PP 68. First published as a full standard in 2018. 2018 by the American Association of State Highway and Transportation Officials. All rights reserved. Duplication is a violation of applicable law.
