Design of a Multimodal Detection System Tested on Tea Impurity Detection Article Swipe
Zhankun Kuang
,
Xiangyang Yu
,
Yuchen Guo
,
Yefan Cai
,
Weibin Hong
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/rs16091590
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/rs16091590
A multimodal detection system with complementary capabilities for efficient detection was developed for impurity detection. The system consisted of a visible light camera, a multispectral camera, image correction and registration algorithms. It can obtain spectral features and color features at the same time and has higher spatial resolution than a single spectral camera. This system was applied to detect impurities in Pu’er tea to verify its high efficiency. The spectral and color features of each pixel in the images of Pu’er tea were obtained by this system and used for pixel classification. The experimental results showed that the accuracy of a support vector machine (SVM) model based on combined features was 93%, which was 7% higher than that based on spectral features only. By applying a median filtering algorithm and a contour detection algorithm to the label matrix extracted from pixel-classified images, except hair, eight impurities were detected successfully. Moreover, taking advantage of the high resolution of a visible light camera, small impurities could be clearly imaged. By comparing the segmented color image with the pixel-classified image, small impurities such as hair could be detected successfully. Finally, it was proved that the system could obtain multiple images to allow a more detailed and comprehensive understanding of the detected items and had an excellent ability to detect small impurities.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/rs16091590
- https://www.mdpi.com/2072-4292/16/9/1590/pdf?version=1714405261
- OA Status
- gold
- Cited By
- 4
- References
- 46
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4396518765
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4396518765Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/rs16091590Digital Object Identifier
- Title
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Design of a Multimodal Detection System Tested on Tea Impurity DetectionWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-04-29Full publication date if available
- Authors
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Zhankun Kuang, Xiangyang Yu, Yuchen Guo, Yefan Cai, Weibin HongList of authors in order
- Landing page
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https://doi.org/10.3390/rs16091590Publisher landing page
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https://www.mdpi.com/2072-4292/16/9/1590/pdf?version=1714405261Direct link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://www.mdpi.com/2072-4292/16/9/1590/pdf?version=1714405261Direct OA link when available
- Concepts
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Multispectral image, Pixel, Artificial intelligence, Computer vision, Computer science, Support vector machine, Image resolution, Impurity, Pattern recognition (psychology), Physics, Quantum mechanicsTop concepts (fields/topics) attached by OpenAlex
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4Total citation count in OpenAlex
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2025: 3, 2024: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W4321213819, https://openalex.org/W4308208628, https://openalex.org/W2062160894, https://openalex.org/W3165764097, https://openalex.org/W4310582166, https://openalex.org/W4380767893, https://openalex.org/W4319656773, https://openalex.org/W4289656389, https://openalex.org/W4223961518, https://openalex.org/W4382133939, https://openalex.org/W2985914417, https://openalex.org/W3210431928, https://openalex.org/W1483395859, https://openalex.org/W1996621989, https://openalex.org/W6765364041, https://openalex.org/W2726355056, https://openalex.org/W2560397974, https://openalex.org/W2793327491, https://openalex.org/W2969854835, https://openalex.org/W1970823366, https://openalex.org/W2035687979, https://openalex.org/W2463202518, https://openalex.org/W2620473470, https://openalex.org/W2979678230, https://openalex.org/W4210463746, https://openalex.org/W4206642698, https://openalex.org/W4388171732, https://openalex.org/W2967038451, https://openalex.org/W4365455655, https://openalex.org/W4313704988, https://openalex.org/W3113678014, https://openalex.org/W4205443268, https://openalex.org/W4391884397, https://openalex.org/W4391307588, https://openalex.org/W4387106618, https://openalex.org/W4321021048, https://openalex.org/W2088252378, https://openalex.org/W6623737539, https://openalex.org/W6602024816, https://openalex.org/W4239510810, https://openalex.org/W3112472111, https://openalex.org/W1974799041, https://openalex.org/W2216946510, https://openalex.org/W2956012779, https://openalex.org/W47803021, https://openalex.org/W2371638596 |
| referenced_works_count | 46 |
| abstract_inverted_index.A | 0 |
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| corresponding_author_ids | https://openalex.org/A5101776455 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 5 |
| corresponding_institution_ids | https://openalex.org/I141649914, https://openalex.org/I157773358 |
| citation_normalized_percentile.value | 0.76170213 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |