An adaptive tire tread depth measurement method based on binocular line-laser

Abstract In this paper, an adaptive method for tire tread depth measurement based on binocular line laser is proposed and implemented. Important challenges such as the interference of wear indicators, the interference of lateral tread grooves, and the adaptability to tires with different specifications are addressed. First, a binocular 3D laser reconstruction platform was set up to capture complete point cloud data of the tire crown. To overcome the scattering and non-uniform brightness of the laser stripes on the tire crown, a sub-pixel algorithm for extracting the centerline of the stripe was developed based on the normal convergence of offset points, which greatly improved the stability and accuracy compared with the gray centroid method and Steger algorithm (by about 25.3% and 12.2%, respectively). In processing the point cloud, a multi-level segmentation strategy for wear indicators was developed, combining geometric feature constraints and region growing, and an adaptive DBSCAN with distance thresholds was used to remove lateral groove points and localize points on the tread. To adapt to different tire specifications, an adaptive algorithm for extracting grooves on the tire base was proposed based on the cross-sectional width of the tire. The experiments used four passenger-car tires of different specifications as validation objects, and these four specifications are all widely used mainstream tire specifications. The absolute error between the measurement results and those obtained using a manual depth gauge is less than 0.2 mm, and the measurement efficiency is improved by 2.5 times compared with the manual method. The method proposed in this paper meets the requirements of industrial online inspection in terms of accuracy, robustness, and generality.