Articles | Volume 22, issue 2
https://doi.org/10.5194/acp-22-1293-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-22-1293-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
25 Jan 2022
Research article |
| 25 Jan 2022
| 25 Jan 2022
New particle formation event detection with Mask R-CNN
Peifeng Su
CORRESPONDING AUTHOR
Department of Geosciences and Geography, University of Helsinki,
00014 Helsinki, Finland
Institute for Atmospheric and Earth System Research (INAR/Physics), Faculty of Science, University of
Helsinki, 00014 Helsinki, Finland
Jorma Joutsensaari
Department of Applied Physics, University of Eastern Finland, P.O. Box 1627, 70211 Kuopio, Finland
Lubna Dada
Extreme Environments Research Laboratory, École Polytechnique Fédérale de Lausanne (EPFL) Valais, 1951 Sion, Switzerland
Laboratory of Atmospheric Chemistry, Paul Scherrer Institute, 5232 Villigen, Switzerland
Martha Arbayani Zaidan
Institute for Atmospheric and Earth System Research (INAR/Physics), Faculty of Science, University of
Helsinki, 00014 Helsinki, Finland
Joint International Research Laboratory of Atmospheric and Earth System Sciences, School of Atmospheric
Sciences, Nanjing University, Nanjing, 210023, China
Tuomo Nieminen
Institute for Atmospheric and Earth System Research (INAR/Physics), Faculty of Science, University of
Helsinki, 00014 Helsinki, Finland
Institute for Atmospheric and Earth System Research (INAR/Forest Sciences), Faculty of Agriculture and Forestry, University of Helsinki, 00014 Helsinki, Finland
Xinyang Li
Institute for Atmospheric and Earth System Research (INAR/Physics), Faculty of Science, University of
Helsinki, 00014 Helsinki, Finland
Yusheng Wu
Institute for Atmospheric and Earth System Research (INAR/Physics), Faculty of Science, University of
Helsinki, 00014 Helsinki, Finland
Stefano Decesari
Institute of Atmospheric and Climate Sciences, National Research Council of Italy (CNR), 40129, Bologna, Italy
Sasu Tarkoma
Department of Computer Science, Faculty of Science, University of
Helsinki, 00014 Helsinki, Finland
Tuukka Petäjä
Institute for Atmospheric and Earth System Research (INAR/Physics), Faculty of Science, University of
Helsinki, 00014 Helsinki, Finland
Joint International Research Laboratory of Atmospheric and Earth System Sciences, School of Atmospheric
Sciences, Nanjing University, Nanjing, 210023, China
Markku Kulmala
Institute for Atmospheric and Earth System Research (INAR/Physics), Faculty of Science, University of
Helsinki, 00014 Helsinki, Finland
Joint International Research Laboratory of Atmospheric and Earth System Sciences, School of Atmospheric
Sciences, Nanjing University, Nanjing, 210023, China
Petri Pellikka
Department of Geosciences and Geography, University of Helsinki,
00014 Helsinki, Finland
Institute for Atmospheric and Earth System Research (INAR/Physics), Faculty of Science, University of
Helsinki, 00014 Helsinki, Finland
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Cited
13 citations as recorded by crossref.
- Appraising the impact of COVID-19 on trading volume of selected vessel types in sub-Saharan Africa O. Olapoju 10.1186/s41072-023-00156-7
- Atmospheric Particle Number Concentrations and New Particle Formation over the Southern Ocean and Antarctica: A Critical Review J. Wang et al. 10.3390/atmos14020402
- Revealing the role of polymer in the robust preparation of the 2,4-dichlorophenoxyacetic acid metastable crystal form by AI-based image analysis L. Fang et al. 10.1016/j.powtec.2022.118077
- Seasonal significance of new particle formation impacts on cloud condensation nuclei at a mountaintop location N. Hirshorn et al. 10.5194/acp-22-15909-2022
- A machine learning method for the prediction of ship motion trajectories in real operational conditions M. Zhang et al. 10.1016/j.oceaneng.2023.114905
- Quantitative analysis of the impact of COVID-19 on ship visiting behaviors to ports- A framework and a case study X. Wang et al. 10.1016/j.ocecoaman.2022.106377
- Liquid detection and instance segmentation based on Mask R-CNN in industrial environment G. Gawdzik & A. Orłowski 10.22630/MGV.2023.32.3.10
- New particle formation event detection with convolutional neural networks X. Zhang et al. 10.1016/j.atmosenv.2024.120487
- Nanoparticle ranking analysis: determining new particle formation (NPF) event occurrence and intensity based on the concentration spectrum of formed (sub-5 nm) particles D. Aliaga et al. 10.5194/ar-1-81-2023
- Retrieval of Multiple Atmospheric Environmental Parameters From Images With Deep Learning P. Su et al. 10.1109/LGRS.2022.3149045
- Untangling the influence of Antarctic and Southern Ocean life on clouds M. Mallet et al. 10.1525/elementa.2022.00130
- Image-to-Image Training for Spatially Seamless Air Temperature Estimation With Satellite Images and Station Data P. Su et al. 10.1109/JSTARS.2023.3256363
- Rapid Nucleation and Growth of Indoor Atmospheric Nanocluster Aerosol during the Use of Scented Volatile Chemical Products in Residential Buildings S. Patra et al. 10.1021/acsestair.4c00118
13 citations as recorded by crossref.
- Appraising the impact of COVID-19 on trading volume of selected vessel types in sub-Saharan Africa O. Olapoju 10.1186/s41072-023-00156-7
- Atmospheric Particle Number Concentrations and New Particle Formation over the Southern Ocean and Antarctica: A Critical Review J. Wang et al. 10.3390/atmos14020402
- Revealing the role of polymer in the robust preparation of the 2,4-dichlorophenoxyacetic acid metastable crystal form by AI-based image analysis L. Fang et al. 10.1016/j.powtec.2022.118077
- Seasonal significance of new particle formation impacts on cloud condensation nuclei at a mountaintop location N. Hirshorn et al. 10.5194/acp-22-15909-2022
- A machine learning method for the prediction of ship motion trajectories in real operational conditions M. Zhang et al. 10.1016/j.oceaneng.2023.114905
- Quantitative analysis of the impact of COVID-19 on ship visiting behaviors to ports- A framework and a case study X. Wang et al. 10.1016/j.ocecoaman.2022.106377
- Liquid detection and instance segmentation based on Mask R-CNN in industrial environment G. Gawdzik & A. Orłowski 10.22630/MGV.2023.32.3.10
- New particle formation event detection with convolutional neural networks X. Zhang et al. 10.1016/j.atmosenv.2024.120487
- Nanoparticle ranking analysis: determining new particle formation (NPF) event occurrence and intensity based on the concentration spectrum of formed (sub-5 nm) particles D. Aliaga et al. 10.5194/ar-1-81-2023
- Retrieval of Multiple Atmospheric Environmental Parameters From Images With Deep Learning P. Su et al. 10.1109/LGRS.2022.3149045
- Untangling the influence of Antarctic and Southern Ocean life on clouds M. Mallet et al. 10.1525/elementa.2022.00130
- Image-to-Image Training for Spatially Seamless Air Temperature Estimation With Satellite Images and Station Data P. Su et al. 10.1109/JSTARS.2023.3256363
- Rapid Nucleation and Growth of Indoor Atmospheric Nanocluster Aerosol during the Use of Scented Volatile Chemical Products in Residential Buildings S. Patra et al. 10.1021/acsestair.4c00118
Latest update: 20 Nov 2024
Short summary
We regarded the banana shapes in the surface plots as a special kind of object (similar to cats) and applied an instance segmentation technique to automatically identify the new particle formation (NPF) events (especially the strongest ones), in addition to their growth rates, start times, and end times. The automatic method generalized well on datasets collected in different sites, which is useful for long-term data series analysis and obtaining statistical properties of NPF events.
We regarded the banana shapes in the surface plots as a special kind of object (similar to cats)...
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