| Issue |
ITM Web Conf.
Volume 45, 2022
2021 3rd International Conference on Computer Science Communication and Network Security (CSCNS2021)
|
|
|---|---|---|
| Article Number | 01018 | |
| Number of page(s) | 9 | |
| Section | Computer Technology and System Design | |
| DOI | https://doi.org/10.1051/itmconf/20224501018 | |
| Published online | 19 May 2022 | |
A stereo optical comparison method for detection of metallic surface defects based on machine vision and laser triangulation
Huaiyin Institute of Technology, Faculty of Computer and Software Engineering, 223003, 1, Meicheng East Road, Huaian, Jiangsu, China
* Corresponding author: 1363467258@qq.com
Quality of metallic surfaces is essential to maintain performance and longevity of industrial products. The surface defects of products like holes and cracks not only affect their appearances and performance, but also may even result in potential danger to human health in some cases. Traditional surface inspection methods for metallic surfaces rely on manual inspection methods by naked eyes or with the help of close circuit cameras, presenting the problems of low efficiency, low accuracy, high labour intensity due to monotonous work. Today manual inspection is not possible in many applications, therefore an automated method with a high accuracy is desirable. In this paper we propose a novel stereo optical comparison method to detect defects on metallic surface based on machine vision and laser triangulation.
© The Authors, published by EDP Sciences, 2022
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.