3D Panel Alignment System Using Deep Learning-Based Structured Light and System Stability and Speedup

  • 葉 勁毅

Student thesis: Doctoral Thesis


Three-dimensional reconstruction technology is often used in industrial inspection to realize the automated production of production lines In this study stereo vision combined with structured light architecture is used to reconstruct the depth information of the object surface for calculation of industrial inspection Stereo vision technology needs to use two cameras After the camera is calibrated the left and right images are at the same image coordinates The projector projects a multi-frequency phase-shift pattern for encoding Through the principle of heterodyne and phase unwrapping a comparison table of left and right image is decoded from left and right pattern The image searches for the corresponding points confirms the corresponding relationship and then restores the depth information by Trigonometric Measurements After obtaining the three-dimensional information image processing and other algorithms are used to perform depth detection angle measurement and offset correction calculations for the object to be measured The two cameras are connected by a synchronization line to achieve synchronized acquisition The calculation process uses multiple threads and pre-established look-up tables to speed up the calculation process to meet the time requirements of the production line After experimental testing the reconstructed point cloud has an accuracy of 0 09mm on the XY axis and 0 01mm on the Z axis and the reconstruction time is about 7 seconds In the second half of the study the corresponding point search and 3D reconstruction were performed by the deep learning method In traditional methods the reconstruction speed can be increased to about 4 5 seconds but the accuracy of the Z axis can only reach 1mm
Date of Award2020
Original languageEnglish
SupervisorJames Jenn-Jier Lien (Supervisor) & Shu-Mei Guo (Supervisor)

Cite this