Proceedings of the6th International Conference on Mechatronics, Materials, Biotechnology and Environment (ICMMBE 2016)

Surface Defect Detection Method of Mechanical Parts Based on Target Feature

Authors
Chengcheng Wang, Shengqi Guan, Wensen Li, Benben Hong, Hong Liang
Corresponding Author
Chengcheng Wang
Available Online September 2016.
DOI
https://doi.org/10.2991/icmmbe-16.2016.45How to use a DOI?
Keywords
Mechanical parts; Surface defect; Target feature; Detection method
Abstract
Through the analysis of surface defect characteristic about mechanical parts, a new method of based on target feature was presented to surface defect detection of mechanical parts. First of all, the test image is processed by mean-filter in order to eliminate the effect of noise on the detection. Secondly, based on the analysis of the characteristic of the surface defect, the segmentation threshold is set, and segment surface defect of mechanical parts. Finally, the effect of noise and outlier on the detection result was eliminated by morphological filtering. Experiment shows that the method can effectively suppress the image background interference, and more accurately realize the surface defect detection of mechanical parts.
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This is an open access article distributed under the CC BY-NC license.

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Proceedings
6th International Conference on Mechatronics, Materials, Biotechnology and Environment (ICMMBE 2016)
Part of series
Advances in Engineering Research
Publication Date
September 2016
ISBN
978-94-6252-228-2
ISSN
2352-5401
DOI
https://doi.org/10.2991/icmmbe-16.2016.45How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Chengcheng Wang
AU  - Shengqi Guan
AU  - Wensen Li
AU  - Benben Hong
AU  - Hong Liang
PY  - 2016/09
DA  - 2016/09
TI  - Surface Defect Detection Method of Mechanical Parts Based on Target Feature
BT  - 6th International Conference on Mechatronics, Materials, Biotechnology and Environment (ICMMBE 2016)
PB  - Atlantis Press
SP  - 229
EP  - 232
SN  - 2352-5401
UR  - https://doi.org/10.2991/icmmbe-16.2016.45
DO  - https://doi.org/10.2991/icmmbe-16.2016.45
ID  - Wang2016/09
ER  -