Research on Key Technology of Diamond Particle Detection Based on Machine Vision
Most traditional methods for detecting the quality of tiny particles are manually detected by measurement tools. Manual detection has the limitations of low efficiency, high false detection rate and low detection accuracy, and is gradually replaced by non-contact measurement. In this paper, a new de...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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EDP Sciences
2018-01-01
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| Series: | MATEC Web of Conferences |
| Online Access: | https://doi.org/10.1051/matecconf/201823202059 |
| _version_ | 1824008853740584960 |
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| author | Liu Xiaomin Mao Jian |
| author_facet | Liu Xiaomin Mao Jian |
| author_sort | Liu Xiaomin |
| collection | DOAJ |
| description | Most traditional methods for detecting the quality of tiny particles are manually detected by measurement tools. Manual detection has the limitations of low efficiency, high false detection rate and low detection accuracy, and is gradually replaced by non-contact measurement. In this paper, a new denoising algorithm based on median and mean filtering is proposed in diamond particle image analysis. Dynamic threshold segmentation is combined with the Canny algorithm for edge extraction. In the edge extraction process of the particle edge, it will lead to non-closed problems. A contour edge location method based on Hough transform is proposed. Thereby, parameters such as particle size, roundness and ellipticity of the diamond particles are measured. |
| first_indexed | 2024-12-18T21:01:54Z |
| format | Article |
| id | doaj.art-41aee789e1db4a73b26a6b9c4fd4f226 |
| institution | Directory Open Access Journal |
| issn | 2261-236X |
| language | English |
| last_indexed | 2024-12-18T21:01:54Z |
| publishDate | 2018-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | MATEC Web of Conferences |
| spelling | doaj.art-41aee789e1db4a73b26a6b9c4fd4f2262022-12-21T20:52:48ZengEDP SciencesMATEC Web of Conferences2261-236X2018-01-012320205910.1051/matecconf/201823202059matecconf_eitce2018_02059Research on Key Technology of Diamond Particle Detection Based on Machine VisionLiu Xiaomin0Mao Jian1School of mechanical and automotive engineering, Shanghai University of Engineering ScienceSchool of mechanical and automotive engineering, Shanghai University of Engineering ScienceMost traditional methods for detecting the quality of tiny particles are manually detected by measurement tools. Manual detection has the limitations of low efficiency, high false detection rate and low detection accuracy, and is gradually replaced by non-contact measurement. In this paper, a new denoising algorithm based on median and mean filtering is proposed in diamond particle image analysis. Dynamic threshold segmentation is combined with the Canny algorithm for edge extraction. In the edge extraction process of the particle edge, it will lead to non-closed problems. A contour edge location method based on Hough transform is proposed. Thereby, parameters such as particle size, roundness and ellipticity of the diamond particles are measured.https://doi.org/10.1051/matecconf/201823202059 |
| spellingShingle | Liu Xiaomin Mao Jian Research on Key Technology of Diamond Particle Detection Based on Machine Vision MATEC Web of Conferences |
| title | Research on Key Technology of Diamond Particle Detection Based on Machine Vision |
| title_full | Research on Key Technology of Diamond Particle Detection Based on Machine Vision |
| title_fullStr | Research on Key Technology of Diamond Particle Detection Based on Machine Vision |
| title_full_unstemmed | Research on Key Technology of Diamond Particle Detection Based on Machine Vision |
| title_short | Research on Key Technology of Diamond Particle Detection Based on Machine Vision |
| title_sort | research on key technology of diamond particle detection based on machine vision |
| url | https://doi.org/10.1051/matecconf/201823202059 |
| work_keys_str_mv | AT liuxiaomin researchonkeytechnologyofdiamondparticledetectionbasedonmachinevision AT maojian researchonkeytechnologyofdiamondparticledetectionbasedonmachinevision |