A computer vision-based dimension measurement method for visual inspection system in smart manufacturing

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.

Name thesis:degree_name

M.S.

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Electrical & Computer Engr

Grantor

University of Illinois Urbana-Champaign

Year dc:date

2025

Author dc:creator

• Liu, Shitao

Contributors dc:contributor

• Do, Minh N.

• Computer Vision
• Inspection
• Metrology
• Smart Manufacturing

Statement dc:rights

• Copyright 2025 Shitao Liu

Language dc:language

en, eng

Handle dc:identifier

https://hdl.handle.net/2142/129793

OAI record oai:identifier

https://www.ideals.illinois.edu/oai-pmh?verb=GetRecord&metadataPrefix=etdms&identifier=oai%3Awww.ideals.illinois.edu%3A2142%2F129793