Laser stripe segmentation and centerline extraction based on 3D scanning imaging

Abstract

Ambient noise and illumination inhomogeneity will seriously affect the high-precision measurement of structured light 3D morphology. To overcome the influences of these factors, a new, to the best of our knowledge, sub-pixel extraction method for the center of laser stripes is proposed. First, an automatic segmentation model of structured light stripe based on the UNet deep learning network and level set is constructed. Coarse segmentation of laser stripes using the UNet network can effectively segment more complex scenes and automatically obtain a prior shape information. Then, the prior information is used as a shape constraint for fine segmentation of the level set, and the energy function of the level set is improved. Finally, the stripe normal field is obtained by calculating the stripe gradient vector, and the center of the stripe is extracted by fusing the gray center of gravity method according to the normal direction of the stripe distribution. The experimental results show that the average width error of different rows of point cloud data of workpieces with different widths is less than 0.3 mm, and the average repeatability extraction error is less than 0.2 mm.