1、Standard Practice for Collecting Images of Pavement Surfaces for Distress Detection AASHTO Designation: PP 68-14 (2016)1 Release: Group 1 (April 2016) American Association of State Highway and Transportation Officials 444 North Capitol Street N.W., Suite 249 Washington, D.C. 20001 TS-5a PP 68-1 AASH
2、TO Standard Practice for Collecting Images of Pavement Surfaces for Distress Detection AASHTO Designation: PP 68-14 (2016)1Release: Group 1 (April 2016) 1. SCOPE 1.1. This practice outlines the procedures for collecting images of pavement surfaces utilizing automated methods for the purpose of distr
3、ess 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 the functionality stipulated herein is considered acceptabl
4、e. 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 platform traveling at or near the prevailing highway speed. 1.3. Th
5、e 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 to establish appropriate safety and health practices and det
6、ermine 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 in.) length. 2.1.2. crack widththe average gap in millimete
7、rs (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. pavement imagea representation of the pavement that descri
8、bes a characteristic (gray scale, color, temperature, elevation, etc.) of a matrix of points (pixels) on the pavement surface. 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 detecti
9、on. Its purpose is to produce consistent data collection. 2016 by the American Association of State Highway and Transportation Officials. All rights reserved. Duplication is a violation of applicable law.TS-5a PP 68-2 AASHTO 3.2. It is recognized that the requirements for the collected image(s) list
10、ed 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 method of measuring performance that is independent of the dete
11、ction 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 rods placed on the pavement at various angles and positions.
12、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 surveyed or rated based on sound engineering principles an
13、d 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 should be 4.25 m (14 ft) wide to include an additional 300
14、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 full-width data. Data beyond the required image width may be
15、 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 project-level collection covers all lanes. 4.2.3. For network d
16、ata 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 images must provide sufficient difference between data point
17、 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 5 mm (0.2 in.) wide, and 85 percent of all cracks 5 mm (0.2
18、 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. The images should be sufficiently void of erroneous differen
19、ces 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 determination of this capability will be made utilizing a minimum of
20、 ten 0.03-km (100-ft) samples of various types that meet the criteria. Note 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 capabilitie
21、s are better defined, separate levels of performance may be established for two or three classes of equipment. 2016 by the American Association of State Highway and Transportation Officials. All rights reserved. Duplication is a violation of applicable law.TS-5a PP 68-3 AASHTO 4.3.3. Detected averag
22、e 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 either illuminated or passive. It may also be a dimension
23、al 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 should be reported as a minimum, along with a unique image
24、 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 affect image analysis, such as the presence of crack seal, rai
25、lroad 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 follow the manufacturers recommendations for calibrating
26、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 and length in all orientations. 6.1.3. Crack delineation
27、(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/temperature): 6.1.6.1. Performance at various sun angles and in
28、tensities; 6.1.6.2. Performance at various speeds; 6.1.6.3. Performance at various vehicle attitudes and distances relative to the pavement; 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
29、 conditions; and 6.1.6.7. Resolution versus position of cracks due to optical distortion. 2016 by the American Association of State Highway and Transportation Officials. All rights reserved. Duplication is a violation of applicable law.TS-5a PP 68-4 AASHTO 6.2. Ground truth for system calibration an
30、d 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 depicting each crack in the section and its unique identifier. In
31、cluded 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 severity categories based on average width and present a comp
32、arison 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 system. They must be trained in equipment operation, includi
33、ng 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 verification program may be supplemented with a validation progr
34、am 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 are then compared for reasonableness with previous runs.
35、A typical implementation of this process would involve 5 km (3 miles) of data collection and be performed monthly. 7. KEYWORDS 7.1. Asphalt pavement surface; automated data collection; pavement distress; pavement images; pavement management. 8. REFERENCES 8.1. AASHTO PP 67, Quantifying Cracks in Asp
36、halt Pavement Surfaces from Collected Images Utilizing Automated Methods. 8.2. ASTM E1656/E1656M, Standard Guide for Classification of Automated Pavement Condition Survey Equipment. 8.3. FHWA. Distress Identification Manual for the Long-Term Pavement Performance Program, FHWA Report RD-03-031. 1This provisional standard was first published in 2010. 2016 by the American Association of State Highway and Transportation Officials. All rights reserved. Duplication is a violation of applicable law.
