Image-Based Analysis of Dense Particle Mixtures via Mask R-CNN
Powders and their mixtures are elemental for many industries (e.g., food, pharmaceutical, mining, agricultural, and chemical). The properties of the manufactured products are often directly linked to the particle properties (e.g., particle size and shape distribution) of the utilized powder mixtures...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-01-01
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| Series: | Eng |
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| Online Access: | https://www.mdpi.com/2673-4117/3/1/7 |
| _version_ | 1797446552131731456 |
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| author | Max Frei Frank Einar Kruis |
| author_facet | Max Frei Frank Einar Kruis |
| author_sort | Max Frei |
| collection | DOAJ |
| description | Powders and their mixtures are elemental for many industries (e.g., food, pharmaceutical, mining, agricultural, and chemical). The properties of the manufactured products are often directly linked to the particle properties (e.g., particle size and shape distribution) of the utilized powder mixtures. The most straightforward approach to acquire information concerning these particle properties is image capturing. However, the analysis of the resulting images often requires manual labor and is therefore time-consuming and costly. Therefore, the work at hand evaluates the suitability of Mask R-CNN—one of the best-known deep learning architectures for object detection—for the fully automated image-based analysis of particle mixtures, by comparing it to a conventional, i.e., not machine learning-based, image analysis method, as well as the results of a trifold manual analysis. To avoid the need of a laborious manual annotation, the training data required by Mask R-CNN are produced via image synthesis. As an example for an industrially relevant particle mixture, endoscopic images from a fluid catalytic cracking reactor are used as a test case for the evaluation of the tested methods. According to the results of the evaluation, Mask R-CNN is a well-suited method for the fully automatic image-based analysis of particle mixtures. It allows for the detection and classification of particles with an accuracy of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>42.7</mn></mrow></semantics></math></inline-formula>% for the utilized data, as well as the characterization of the particle shape. Also, it enables the measurement of the mixture component particle size distributions with errors (relative to the manual reference) as low as <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mo>−</mo><mn>2</mn><mo>±</mo><mn>5</mn></mrow></semantics></math></inline-formula> for the geometric mean diameter and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mo>−</mo><mn>6</mn><mo>±</mo><mn>5</mn><mo>%</mo></mrow></semantics></math></inline-formula> for the geometric standard deviation of the <i>dark</i> particle class of the utilized data, as well as <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mo>−</mo><mn>8</mn><mo>±</mo><mn>4</mn><mo>%</mo></mrow></semantics></math></inline-formula> for the geometric mean diameter and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mo>−</mo><mn>6</mn><mo>±</mo><mn>2</mn><mo>%</mo></mrow></semantics></math></inline-formula> for the geometric standard deviation of the <i>light</i> particle class of the utilized data. Source code, as well as training, validation, and test data publicly available. |
| first_indexed | 2024-03-09T13:43:07Z |
| format | Article |
| id | doaj.art-436b304e7c56421897fe6ea4cc82805c |
| institution | Directory Open Access Journal |
| issn | 2673-4117 |
| language | English |
| last_indexed | 2024-03-09T13:43:07Z |
| publishDate | 2022-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Eng |
| spelling | doaj.art-436b304e7c56421897fe6ea4cc82805c2023-11-30T21:04:14ZengMDPI AGEng2673-41172022-01-0131789810.3390/eng3010007Image-Based Analysis of Dense Particle Mixtures via Mask R-CNNMax Frei0Frank Einar Kruis1Institute of Technology for Nanostructures (NST) and Center for Nanointegration Duisburg-Essen (CENIDE), University of Duisburg-Essen, D-47057 Duisburg, GermanyInstitute of Technology for Nanostructures (NST) and Center for Nanointegration Duisburg-Essen (CENIDE), University of Duisburg-Essen, D-47057 Duisburg, GermanyPowders and their mixtures are elemental for many industries (e.g., food, pharmaceutical, mining, agricultural, and chemical). The properties of the manufactured products are often directly linked to the particle properties (e.g., particle size and shape distribution) of the utilized powder mixtures. The most straightforward approach to acquire information concerning these particle properties is image capturing. However, the analysis of the resulting images often requires manual labor and is therefore time-consuming and costly. Therefore, the work at hand evaluates the suitability of Mask R-CNN—one of the best-known deep learning architectures for object detection—for the fully automated image-based analysis of particle mixtures, by comparing it to a conventional, i.e., not machine learning-based, image analysis method, as well as the results of a trifold manual analysis. To avoid the need of a laborious manual annotation, the training data required by Mask R-CNN are produced via image synthesis. As an example for an industrially relevant particle mixture, endoscopic images from a fluid catalytic cracking reactor are used as a test case for the evaluation of the tested methods. According to the results of the evaluation, Mask R-CNN is a well-suited method for the fully automatic image-based analysis of particle mixtures. It allows for the detection and classification of particles with an accuracy of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>42.7</mn></mrow></semantics></math></inline-formula>% for the utilized data, as well as the characterization of the particle shape. Also, it enables the measurement of the mixture component particle size distributions with errors (relative to the manual reference) as low as <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mo>−</mo><mn>2</mn><mo>±</mo><mn>5</mn></mrow></semantics></math></inline-formula> for the geometric mean diameter and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mo>−</mo><mn>6</mn><mo>±</mo><mn>5</mn><mo>%</mo></mrow></semantics></math></inline-formula> for the geometric standard deviation of the <i>dark</i> particle class of the utilized data, as well as <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mo>−</mo><mn>8</mn><mo>±</mo><mn>4</mn><mo>%</mo></mrow></semantics></math></inline-formula> for the geometric mean diameter and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mo>−</mo><mn>6</mn><mo>±</mo><mn>2</mn><mo>%</mo></mrow></semantics></math></inline-formula> for the geometric standard deviation of the <i>light</i> particle class of the utilized data. Source code, as well as training, validation, and test data publicly available.https://www.mdpi.com/2673-4117/3/1/7imaging particle analysisfluid catalytic crackingautomatic particle mixture analysisMask R-CNNimage synthesisHough transform |
| spellingShingle | Max Frei Frank Einar Kruis Image-Based Analysis of Dense Particle Mixtures via Mask R-CNN Eng imaging particle analysis fluid catalytic cracking automatic particle mixture analysis Mask R-CNN image synthesis Hough transform |
| title | Image-Based Analysis of Dense Particle Mixtures via Mask R-CNN |
| title_full | Image-Based Analysis of Dense Particle Mixtures via Mask R-CNN |
| title_fullStr | Image-Based Analysis of Dense Particle Mixtures via Mask R-CNN |
| title_full_unstemmed | Image-Based Analysis of Dense Particle Mixtures via Mask R-CNN |
| title_short | Image-Based Analysis of Dense Particle Mixtures via Mask R-CNN |
| title_sort | image based analysis of dense particle mixtures via mask r cnn |
| topic | imaging particle analysis fluid catalytic cracking automatic particle mixture analysis Mask R-CNN image synthesis Hough transform |
| url | https://www.mdpi.com/2673-4117/3/1/7 |
| work_keys_str_mv | AT maxfrei imagebasedanalysisofdenseparticlemixturesviamaskrcnn AT frankeinarkruis imagebasedanalysisofdenseparticlemixturesviamaskrcnn |