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Research on Color Uniformity and Seam Detection of Standard Test Paper Based on Machine Vision
- Source :
- 2021 IEEE 4th Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC).
- Publication Year :
- 2021
- Publisher :
- IEEE, 2021.
-
Abstract
- For the detection of product color uniformity and seam texture defects in the production process of test paper, detection methods are proposed respectively. For the detection of the color uniformity, a uniformity detection method based on dynamic light correction is proposed. The illumination correction image is used for color dynamic compensation of the test paper image to solve the influence of uneven illumination on the test paper. For the detection of texture defects such as seams, a texture defect detection method based on gray gradient aggregation intensity is proposed. This method uses one-way Gaussian convolution to strengthen features, and then calculates the gradient, divides the region and calculates the gradient extremum aggregation strength to judge texture defects, which can quickly and efficiently find seam defects. The experimental results show that the above methods have obvious effects on the color uniformity and seam detection of the test paper. The color uniformity accuracy rate is 96%, the seam detection accuracy rate is 97%. The total detection time of a single test paper is about 90ms. The algorithm is real-time and accurate, and can be used in actual production detection.
- Subjects :
- Computer science
business.industry
Machine vision
Texture (cosmology)
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
Paper based
Convolution
Compensation (engineering)
Robustness (computer science)
Standard test
Computer vision
Artificial intelligence
business
Intensity (heat transfer)
ComputingMethodologies_COMPUTERGRAPHICS
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- 2021 IEEE 4th Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC)
- Accession number :
- edsair.doi...........f9d7fbf0c388f6920ddc548653ec117c