Abstract: Aiming at the problem of multi-dimensional index extraction of pavement potholes in low light and weak texture environment, as well as the application defects of traditional methods and laser technology, the binocular structured light 3D imaging system is used to obtain pavement pit images, and the RANSAC algorithm is used to fit the road plane, segment the pit point cloud, intersect with the fitted plane as the contour point of the top surface of the pit, sort by the two-way nearest point search method, and calculate the area by the contour point method after counter-clockwise connection. Extract the depth of each data point to calculate the pit depth index. The microelement approximation method is used to slice the pit equidistant and calculate the volume of the pit. BPA algorithm is used to reconstruct the surface to provide a more intuitive data perspective for related application scenarios. The maximum relative errors of depth of pit, area, volume in normal light and weak light and weak texture environment compare with manual measurement results are 2.73%, 6.50%, 8.60%, 2.99%, 5.25% and 8.28%,respectively. According to the calculation index, the severity evaluation of the pit meets the consistency standard of pit evaluation compared with manual measurement. It shows the applicability of equipment and detection methods to weak light and weak texture environment, and can provide data support and technical support for pothole evaluation, repair material estimation, maintenance priority determination, pavement maintenance planning and formulation.

Key words: weak light and weak texture, binocular structured light 3D imaging, pavement potholes, multi-dimensional index extraction, detection