| Issue |
E3S Web Conf.
Volume 252, 2021
2021 International Conference on Power Grid System and Green Energy (PGSGE 2021)
|
|
|---|---|---|
| Article Number | 02026 | |
| Number of page(s) | 5 | |
| Section | Research and Development of Electrical Equipment and Energy Nuclear Power Devices | |
| DOI | https://doi.org/10.1051/e3sconf/202125202026 | |
| Published online | 23 April 2021 | |
Image processing based on the detection of external defects of fan tower Weld
1 Electrical Engineering, Shanghai Dian Ji university, Shanghai, Shanghai, 200000, China
2 Machinery Industry, Shanghai Dian Ji university, Shanghai, Shanghai, 200000, China
* e-mail: suny@sdju.edu.cn
With the continuous development of wind power generation technology and the continuous increase in the demand for electric energy, the height of the fan tower is more and more demanding. It is very important to detect the weld produced in the welding process of fan tower. In this paper, an algorithm for weld defect detection based on traditional image processing and convolutional neural network is proposed. Firstly, the traditional image processing algorithm is used to gray the weld image collected by industrial camera. Then, the gray image of welding seam is enhanced to improve the visual effect and clear the image, which is convenient for further processing and analysis of the image by computer. Finally, the image is used as the input of the trained convolution neural network to judge whether there are defects outside the weld.
© The Authors, published by EDP Sciences, 2021
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.