AN ALGORITHM FOR DETECTING STAINS ON MASK SURFACE

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Published: 2021-12-14

Page: 46-53


YANG LIAN *

College of Mathematics and Finance, Hunan University of Humanities, Science and Technology, Loudi 417000, China.

SONG XI

College of Mathematics and Finance, Hunan University of Humanities, Science and Technology, Loudi 417000, China.

*Author to whom correspondence should be addressed.


Abstract

To accurately identify the residual stains on the surface of masks, a new algorithm based on digital image processing technology was proposed. Firstly, the mask image is processed by illumination and denoising, and the contrast of the mask image is improved by image enhancement. Then, the mask image is segmented by the threshold iteration method, and the preprocessed image is obtained. To compare the effect of different edge detection operators on mask stain recognition, six commonly used operators are used to detect the edge of the preprocessed image, and then the connected regions are marked by the connected region algorithm. Finally, the connected domain is used to determine whether there are residual stains on the surface of the mask, and the results judged as stains are used for image location. The experimental results show that, the algorithm in this paper does not need to train the positive and negative samples in advance, but it can obtain roughly the same accuracy as the neural network method.

Keywords: Stain detection, digital image processing, edge detection


How to Cite

LIAN, Y., & XI, S. (2021). AN ALGORITHM FOR DETECTING STAINS ON MASK SURFACE. Journal of Basic and Applied Research International, 27(8), 46–53. Retrieved from https://ikprress.org/index.php/JOBARI/article/view/7253

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References

Dang Yongqiang. Research on mask defect detection and classification algorithm based on deep learning [D]. Xi'an University of Technology; 2018.

Tang Qi. Mask surface defect detection based on deep learning [D]. Shenzhen University; 2019.

Liu Chunping, Jian Yaobo, Liu Chengxiang, Zhang Min, Ruan Shuangchen. A stain recognition algorithm and its implementation [J]. Telecommunication Technology; 2006(03): 74-77.

Xiao Menghui, Li Juncheng, Peng Yangxi. A crack detection algorithm for ceramic bottle inner wall [J]. China Ceramic Industry. 2020;27 (04):43-48.

Kumari A, Thomas P J, Sahoo SK. Single image fog removal using gamma transformation and median filtering[C]// 2014 Annual IEEE India Conference (INDICON). IEEE; 2019.

Hall G, Terrell T J, et al. Transputer Implementation of the Radon Transform for Image Enhancement [J]. IEEE ICASSP- 89, 1989;3:1548-1551.

Peter A Toft. Using the Generalized Radon Transform for Detection of Curves in Noisy Images [J]. IEEE ICASSP-96. 1996;4:2219-2222.

Shi T, Kong J, Wang X, et al. Improved Roberts operator for detecting surface defects of heavy rails with superior precision and efficiency [J]. High Technology Letters. 2016;02(v.22):97- 104.

Zhang JY, Yan C, Huang XX. Edge detection of images based on improved Sobel operator and genetic algorithms[C]// 2009 International Conference on Image Analysis and Signal Processing. IEEE; 2009.

Zhou RG, YuH, Cheng Y, et al. Quantum image edge extraction based on improved Prewitt operator [J]. Quantum Information Processing. 2019;18(9).

Wang X. Laplacian Operator-Based Edge Detectors [J]. IEEE Transactions on Pattern Analysis & Machine Intelligence. 2007;29(5):886-90.

Xiao L, Wang H, Liu J. Sub-pixel edge detect technique based on LOG operator [J]. Journal of Baotou University of Iron and Steel Technology; 2002.

Li E S, Zhu SL, Zhu BS, et al. An Adaptive Edge-Detection Method Based on the Canny Operator [C]// International Conference on Environmental Science & Information Application Technology. IEEE; 2009.