Drink Bottle Defect Detection Based on Machine Vision Large Data Analysis
- https://doi.org/10.2991/aiea-16.2016.37How to use a DOI?
- Machine vision; large data; beverage bottle; defect detection; multi-sensor.
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.
- © 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/).
Cite this article
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 - https://doi.org/10.2991/aiea-16.2016.37 ID - Wang2016/11 ER -