TY - JOUR
T1 - 3D Reconstruction of High Reflective Welding Surface Based on Binocular Structured Light Stereo Vision
AU - Li, Baizhen
AU - Xu, Zhijie
AU - Gao, Feng
AU - Cao, Yanlong
AU - Dong, Quancheng
N1 - Funding Information:
Funding: This research was funded by the Key R&D project of Shandong Province, grant number 2020CXGC010206.
Publisher Copyright:
© 2022 by the authors. Licensee MDPI, Basel, Switzerland.
PY - 2022/2/20
Y1 - 2022/2/20
N2 - The inspection of welding surface quality is an important task for welding work. With the development of product quality inspection technology, automated and machine vision-based inspection have been applied to more industrial application fields because of its non-contact, con-venience, and high efficiency. However, challenging material and optical phenomena such as high reflective surface areas often present on welding seams tend to produce artifacts such as holes in the reconstructed model using current visual sensors, hence leading to insufficiency or even errors in the inspection result. This paper presents a 3D reconstruction technique for highly reflective welding surfaces based on binocular style structured light stereo vision. The method starts from capturing a fully lit image for identifying highly reflective regions on a welding surface using conventional com-puter vision models, including gray-scale, binarization, dilation, and erosion. Then, fringe projection profilometry is used to generate point clouds on the interested area. The mapping and alignment from 2D image to 3D point cloud is then established to highlight features that are vital for eliminating “holes”—large featureless areas—caused by high reflections such as the specular mirroring effect. A two-way slicing method is proposed to operate on the refined point cloud, following the concept of dimensionality reduction to project the sliced point cloud onto different image planes before a Smoothing Spline model is applied to fit the discrete point formed by projection. The 3D coordinate values of points in the “hole” region are estimated according to the fitted curves and appended to the original point cloud using iterative algorithms. Experiment results verify that the proposed method can accurately reconstruct a wide range of welding surfaces with significantly improved precision.
AB - The inspection of welding surface quality is an important task for welding work. With the development of product quality inspection technology, automated and machine vision-based inspection have been applied to more industrial application fields because of its non-contact, con-venience, and high efficiency. However, challenging material and optical phenomena such as high reflective surface areas often present on welding seams tend to produce artifacts such as holes in the reconstructed model using current visual sensors, hence leading to insufficiency or even errors in the inspection result. This paper presents a 3D reconstruction technique for highly reflective welding surfaces based on binocular style structured light stereo vision. The method starts from capturing a fully lit image for identifying highly reflective regions on a welding surface using conventional com-puter vision models, including gray-scale, binarization, dilation, and erosion. Then, fringe projection profilometry is used to generate point clouds on the interested area. The mapping and alignment from 2D image to 3D point cloud is then established to highlight features that are vital for eliminating “holes”—large featureless areas—caused by high reflections such as the specular mirroring effect. A two-way slicing method is proposed to operate on the refined point cloud, following the concept of dimensionality reduction to project the sliced point cloud onto different image planes before a Smoothing Spline model is applied to fit the discrete point formed by projection. The 3D coordinate values of points in the “hole” region are estimated according to the fitted curves and appended to the original point cloud using iterative algorithms. Experiment results verify that the proposed method can accurately reconstruct a wide range of welding surfaces with significantly improved precision.
KW - 3D reconstruction
KW - High reflect
KW - Point cloud
KW - Structured light
UR - http://www.scopus.com/inward/record.url?scp=85125148500&partnerID=8YFLogxK
U2 - 10.3390/machines10020159
DO - 10.3390/machines10020159
M3 - Article
AN - SCOPUS:85125148500
VL - 10
JO - Machines
JF - Machines
SN - 2075-1702
IS - 2
M1 - 159
ER -