Pavement distortions, such as rutting and shoving, are the common pavement distress problems that need to be inspected and repaired in a timely manner to ensure ride quality and traffic safety. This paper introduces a real-time, low-cost inspection system devoted to detecting these distress features using high-speed 3D transverse scanning techniques. The detection principle is the dynamic generation and characterization of the 3D pavement profile based on structured light triangulation. To improve the accuracy of the system, a multi-view coplanar scheme is employed in the calibration procedure so that more feature points can be used and distributed across the field of view of the camera. A sub-pixel line extraction method is applied for the laser stripe location, which includes filtering, edge detection and spline interpolation. The pavement transverse profile is then generated from the laser stripe curve and approximated by line segments. The second-order derivatives of the segment endpoints are used to identify the feature points of possible distortions. The system can output the real-time measurements and 3D visualization of rutting and shoving distress in a scanned pavement.

Rutting and pothole are the common pavement distress problems that need to be timely inspected and repaired to ensure ride quality and safe traffic. This paper introduces a real-time, automated inspection system devoted for detecting these distress features using high-speed transverse scanning. The detection principle is based on the dynamic generation and characterization of 3D pavement profiles obtained from structured light measurements. The system implementation mainly involves three tasks: multi-view coplanar calibration, sub-pixel laser stripe location, and pavement distress recognition. The multi-view coplanar scheme was employed in the calibration procedure to increase the feature points and to make the points distributed across the field of view of the camera, which greatly improves the calibration precision. The laser stripe locating method was implemented in four steps: median filtering, coarse edge detection, fine edge adjusting, stripe curve mending and interpolation by cubic splines. The pavement distress recognition algorithms include line segment approximation of the profile, searching for the feature points, and parameters calculations. The parameter data of a curve segment between two feature points, such as width, depth and length, were used to differentiate rutting, pothole, and pothole under different constraints. The preliminary experiment results show that the system is capable of locating these pavement distresses, and meets the needs for real-time and accurate pavement inspection.

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Cracking is a major pavement distress that jeopardizes road serviceability and traffic safety. Automated pavement distress survey (APDS) systems have been developed using digital imaging technology to replace human surveys for more timely and accurate inspections. Most APDS systems require special lighting devices to illuminate pavements and prevent shadows of roadside objects that distort cracks in the image. Most artificial lighting devices are laser based, and are either hazardous to unprotected people or require dedicated power supplies on the vehicle. This study was aimed to develop a new imaging system that can scan pavement surface at highway speed and determine the level of severity of pavement cracking without using any artificial lighting. The new system consists of dual line-scan cameras that are installed side by side to scan the same pavement area as the vehicle moves. Cameras are controlled with different exposure settings so that both sunlit and shadowed areas can be visible in two separate images. The paired images contain complementary details useful for reconstructing an image in which the shadows are eliminated. This paper intends to present (1) the design of the dual line-scan camera system, (2) a new calibration method for line-scan cameras to rectify and register paired images, (3) a customized image-fusion algorithm that merges the multi-exposure images into one shadow-free image for crack detection, and (4) the results of the field tests on a selected road over a long period.

This paper presents a new line-scan imaging system for automated measurements of road crack. The system consists of off-the-shelf hardware for real-time image acquisition and the customized image-analysis software for crack detection. A line-scan camera with 2 k pixels, a GigE interface, and a line rate up to 36 kHz was used to scan 3.6-m wide pavements at highway speeds, and a laser line projector was used to cast a transverse beam that overlays the scanline of the camera to eliminate shadows of vehicles and roadside objects and to maintain consistent lighting conditions. In the crack detection algorithms, a pavement image was first divided into grids of 8×8 pixels, and each grid was classified either as a non-crack or crack grid (called seed) using the pixel information of the grid and the overall background. Then, seeds in the vicinity were connected based on geometrical and intensity constrains. The connected seeds served as a candidate for a crack, which were further verified by using the contrast to the pixels along its trace. The paper also reports the experimental results on a designated pavement that was manually rated by an expert, and scanned three-times by the system. The statistic analysis showed that the difference in crack length between the manual and automatic measurements was less than 10 %, and no significant difference among the multiple scans by the system.

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