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Efficient Anti-Glare Ceramic Decals Defect Detection by Incorporating Homomorphic Filtering

Xin Chen1, Ying Zhang2, Lang Lin1, Junxiang Wang2,*, Jiangqun Ni3
1 Southeast Digital Economic Development Institute, Quzhou, 324000, China
2 School of Mechanical and Electronic Engineering, Jingdezhen Ceramic Institute, Jingdezhen, 333400, China
3 School of Data and Computer Science, Sun Yat-Sen University, Canton, 510000, China
* Corresponding Author: Junxiang Wang. Email:

Computer Systems Science and Engineering 2021, 36(3), 551-564. https://doi.org/10.32604/csse.2021.014495

Received 23 September 2020; Accepted 17 November 2020; Issue published 18 January 2021

Abstract

Nowadays the computer vision technique has widely found applications in industrial manufacturing process to improve their efficiency. However, it is hardly applied in the field of daily ceramic detection due to the following two key reasons: (1) Low detection accuracy as a result of ceramic glare, and (2) Lack of efficient detection algorithms. To tackle these problems, a homomorphic filtering based anti-glare ceramic decals defect detection technique is proposed in this paper. Considering that smooth ceramic surface usually causes glare effects and leads to low detection results, in our approach, the ceramic samples are taken in low light environment and their luminance and details restored by a homomorphic filtering based image enhancement technique. With relatively high quality preprocessed images, an effective ceramic decal defect detection algorithm is then designed to rapidly locate those out-of-bounds defects and further estimate their size. The experimental results show that the proposed scheme could achieve its desired performance.

Keywords

Ceramic glaring; homomorphic filtering; ceramic decal border extraction; out-of-bounds detection

Cite This Article

X. Chen, Y. Zhang, L. Lin, J. Wang and J. Ni, "Efficient anti-glare ceramic decals defect detection by incorporating homomorphic filtering," Computer Systems Science and Engineering, vol. 36, no.3, pp. 551–564, 2021.

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This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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