A computer vision-based dimension measurement method for visual inspection system in smart manufacturing
Liu, Shitao
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https://hdl.handle.net/2142/129793
Description
Title
A computer vision-based dimension measurement method for visual inspection system in smart manufacturing
Author(s)
Liu, Shitao
Issue Date
2025-05-09
Director of Research (if dissertation) or Advisor (if thesis)
Do, Minh N.
Department of Study
Electrical & Computer Eng
Discipline
Electrical & Computer Engr
Degree Granting Institution
University of Illinois Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Keyword(s)
Computer Vision
Inspection
Metrology
Smart Manufacturing
Language
eng
Abstract
Accurate dimension inspection is crucial in manufacturing. The proper functionality of the manufactured products is premised on the accurate dimension recognization, localization, and measurement. Traditionally, this task can be conducted by a trained technician. However, as the global electronics market grows and the development of semi-conductor industry progresses, the manufactured products become smaller in size but larger in quantity, which poses a significant challenge to the manufacturers. Thus, developing and deploying an automated inspection system with high accuracy and fidelity is needed to ensure the smooth operation of these manufacturers. In this thesis, I develop a dimension inspection system based on applying computer vision techniques to analyzing the photos of manufactured parts to solve this question. With CAD drawings and sample part images provided by Foxconn Interconnect Technology (FIT), I design a workflow to facilitate the inspection process, from critical feature recognition to data reporting. The backbone of this inspection system is based on image-level signal filtering and geometry detection to ensure the flexibility and generalizability across different CAD designs. The performance of this inspection system was tested on a large dataset of six different dimensions from the USB-C connector FIT manufactures, as well as our proprietary 3D printing dataset, and the result shows the industrial application potential of this system.
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