Abstract

Automatic detection dramatically improves the efficiency and accuracy of asphalt pavement inspection. However, the existing detection systems cannot coordinate the balance between efficiency and accuracy. Three-dimensional pavement detection was proposed and developed in this article. The theoretical model of camera imaging was first established based on optical triangulation. The camera’s intrinsic and extrinsic parameters were evaluated by utilizing a checkerboard calibration target, and the camera lens distortion was also considered to ensure the accuracy of the three-dimensional measurements. The subpixel coordinates of the single-stripe center were extracted based on two-dimensional image processing technology, and then the pixel coordinates were converted into real-world elevation coordinates based on the camera imaging model. The cubic spline method and the three-dimensional data filtering method based on the pavement surface profile were applied to restore the data and remove the noise, respectively. Then the three-dimensional reconstruction was achieved based on Delaunay triangulation algorithm. We expect that this new system can significantly reduce the costs without decreasing the test accuracy, thus making large-scale engineering applications viable.

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