Quality Inspection of Necked-in Area in Pen Point based on Machine Vision

DOI

10.2991/icadme-17.2017.6How to use a DOI?

Keywords

Halcon; Machine vision; Edge detection; Size detection; Defect detection

Abstract

In order to solve the low efficiency and low accuracy of manual inspection in the quality of necked-in area in pen point, a program of pen point quality inspection was developed based on machine vision in Halcon platform. Firstly, use the median filter to remove the image noise and enhance the contrast of the image; then get the object region of the image through Blob analysis and use edge detection to extract the sub-pixel edges of the object region; finally, use the least square method to fit the edges, and detect the defects by the difference image algorithm. Experimental results show that the program can measure the thickness of the necked-in area quickly and accurately, and identify the defect efficiently.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Liping Tang
AU  - Fen Chen
AU  - Hengchang Guo
PY  - 2017/07
DA  - 2017/07
TI  - Quality Inspection of Necked-in Area in Pen Point based on Machine Vision
BT  - Proceedings of the 2017 7th International Conference on Advanced Design and Manufacturing Engineering (ICADME 2017)
PB  - Atlantis Press
SP  - 25
EP  - 30
SN  - 2352-5401
UR  - https://doi.org/10.2991/icadme-17.2017.6
DO  - 10.2991/icadme-17.2017.6
ID  - Tang2017/07
ER  -