Drink Bottle Defect Detection Based on Machine Vision Large Data Analysis

DOI

10.2991/aiea-16.2016.37How to use a DOI?

Keywords

Machine vision; large data; beverage bottle; defect detection; multi-sensor.

Abstract

Aiming at the problem of low efficiency, low quality and uncertainty of the subjective control of the beverage bottle defect, this paper designs a kind of beverage bottle defect detection based on machine vision large data analysis and multi-sensor information fusion. System. At the same time, a large data sample base is set up in the image data of the beverage bottle product. When the quality of the new beverage bottle is detected, a plurality of features of the image of the beverage bottle are extracted by machine learning and then compared with the large data sample database to identify the possible The existence of bottlenecks, improve the quality of beverage bottles detection efficiency. Through the use of the system to detect and use the artificial test to compare the test, fully demonstrated the system in the beverage bottle flaw detection of high efficiency and high pass rate, reached the beverage bottle product testing and packaging automation requirements are very good Of the application value.

Copyright

© 2016, 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  - Yuesheng Wang
AU  - Hua Li
PY  - 2016/11
DA  - 2016/11
TI  - Drink Bottle Defect Detection Based on Machine Vision Large Data Analysis
BT  - Proceedings of the 2016 International Conference on Artificial Intelligence and Engineering Applications
PB  - Atlantis Press
SP  - 197
EP  - 201
SN  - 2352-538X
UR  - https://doi.org/10.2991/aiea-16.2016.37
DO  - 10.2991/aiea-16.2016.37
ID  - Wang2016/11
ER  -