Study on defect spot recognition method in metal soldering based on intelligent artificial vision

DOI

10.2991/amcce-15.2015.213How to use a DOI?

Keywords

intelligent artificial vision; metal soldering; background subtraction; two value image

Abstract

During the process of defect spot recognition among metal solder joint, if the defect spot is responsible characteristic within small region, the traditional identification method of metal solder joint defect spot is based on sparse representation, unable to express details of characteristics in small region accurately. An optimized metal solder defect spot identification model is proposed, based on the similar triangle principle to derive the relationship between the defect spot depth and weld area, using back projection map of background subtraction graph and color histogram to detect the defect spot region, and convert RBG color space to HSV color space, the color histogram in HSV space is extracted, and the brightness values of the region meets requirement need to be modified, so as to obtain the back projection image of color histogram after processing, and two value image of defect spots detection, with the algorithm based on 7Hu moment vector, on the basis of the solder joint defect spot detection two value image, through acquiring contour of defect spot to match the template in the library, so as to achieve recognition of defect spot in metal solder joint.

Copyright

© 2015, 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  - Zhaoyu Wu
PY  - 2015/04
DA  - 2015/04
TI  - Study on defect spot recognition method in metal soldering based on intelligent artificial vision
BT  - Proceedings of the 2015 International Conference on Automation, Mechanical Control and Computational Engineering
PB  - Atlantis Press
SN  - 1951-6851
UR  - https://doi.org/10.2991/amcce-15.2015.213
DO  - 10.2991/amcce-15.2015.213
ID  - Wu2015/04
ER  -