With Fugro's Pave2D system, images are recorded in JPEG format, effectively cutting each image size down to approximately 0.5-1.5 MB, reducing data storage requirements of the on-board removable hard drives. Two high-resolution monochrome digital cameras are utilized for the capture of the entire pavement lane width (up to 14 feet) while being triggered by the onboard DMI to collect a continuous stream of pavement imagery. Optimally angled and camera-synchronized strobe lights are used to provide artificial lighting that is several times the intensity of natural sunlight.
High camera resolutions of 1392 x 1040 pixels enable the recognition and identification of cracks down to 2 millimeters (0.08 inches) in width. A synchronized, high-speed shutter setting for the two pavement cameras, in combination with the camera's rigid extension arm mounts, ensures consistent sharp pavement images (that are free of any image blurring) are taken regardless of the ARAN’s traveling speed. The high resolution will ensure that transverse, longitudinal and area distresses of even low severity can be identified accurately and in accordance with any distress rating criteria defined by the client.
In order to ensure good quality images and prevent reruns, live streaming of the pavement images has been integrated into the ARAN Control System (ACS) fo operators to view.
Fugro’s Pave3D system creates a detailed 3D model of the road surface. This 3D technology allows fully automated pavement condition assessment of both asphalt and concrete surfaces over 13 feet (4m) in width day or night at speeds up to 62 mph (100 km/h).
Cracking and other distresses are extracted from the 3D profile data. The system uses depth information for each crack to confirm if the crack has depth compared to the road surface. This significantly reduces false positives, and greatly increases the reliability and repeatability of the automated detection results from the Pave3D system.
One of the major differences and advantages of the Pave3D system over 2D systems is its ability to accurately discover joints and tining on rigid (concrete) surfaces. The joints and tining are then excluded from the crack detection information and faulting can be accurately calculated at each joint with multiple measurements across the lane. The depth information is also important for automated pothole detection. Potholes can not only be detected but all of dimensions can be calculated and used for maintenance crews to estimate fill amounts.
With the texture calculated for the full road surface, this 3D map allows improved algorithms to accurately detect areas of raveling and bleeding. This will give less subjective calculations that had previously been done on human discretion. New and improved algorithms are continually being developed with this 3D information and with this comes detection for other previous manual measurements such as sealed cracks and patching.
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