Research and Application of Machine Vision Technology in Workpiece Detection and Recognition
- https://doi.org/10.2991/acsr.k.191223.036How to use a DOI?
- defect defecting, cluster mode, image identification
This paper adopt image recognition to realize the defect detection of small probability defect parts in cluster mode. In this mode, parts with small probability of defects were identified one by one to be recognized. Different lighting conditions were tried to determine what kind of lighting should be used for auxiliary lighting. By means of steps such as gray scale stretching and threshold segmentation, the collected image could be used as binary graph suitable for defect detection after preprocessing. The characteristics of the bolt itself were used as the identification condition to match the measured item with the template. Then the difference information between the matching result and the template was calculated by difference shadow method. And according to this information, whether the product to be tested is qualified is determined.
- © 2019, 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 - Xiangwei Chen AU - Tiantian Zhang PY - 2019 DA - 2019/12/24 TI - Research and Application of Machine Vision Technology in Workpiece Detection and Recognition BT - Proceedings of the 2019 International Conference on Big Data, Electronics and Communication Engineering (BDECE 2019) PB - Atlantis Press SP - 153 EP - 159 SN - 2352-538X UR - https://doi.org/10.2991/acsr.k.191223.036 DO - https://doi.org/10.2991/acsr.k.191223.036 ID - Chen2019 ER -