Surface defects 3D localization for fluorescent magnetic particle inspection via regional reconstruction and partial-in-complete point clouds registration

Accurate three-dimensional localization of defects plays a crucial role in the automated defect marking and grinding process. However, this task becomes increasingly intricate when precisely recovering the three-dimensional information of small defects due to the presence of noise and damage in point clouds generated through three-dimensional reconstruction. Simultaneously, separately reconstructing defects at different locations on the component would significantly impact efficiency. This study focuses on fluorescent magnetic particle inspection, with the aim of enhancing the accuracy of fine crack localization and multi defects reconstruction efficiency. Its basic idea is to align the precise complete point cloud with the regionally reconstructed noisy point cloud of a component, achieving its spatial positioning within the visual system while eliminating noise and imperfections. We commence by discussing various methods for acquiring offline complete point clouds and their respective advantages and disadvantages. To enhance flexibility and minimize regional reconstruction noise, we have devised a multi-camera vision system and optimized a multi-view stereo reconstruction algorithm. We introduce a filtering function for culling back-facing points during the sparse point cloud projection process. The feasibility of our approach is empirically validated, with a particular focus on its precision and efficiency. Experimental results indicate that our method can execute within 5 s, with a relative error of less than 2 millimeters for 95% of the points and a root mean square error less than 1 millimeter. Our approach exhibits significant advantages in addressing noise issues associated with point cloud data generated through image-based 3D reconstruction processes.