Articles | Volume 22, issue 2
Atmos. Chem. Phys., 22, 1293–1309, 2022
https://doi.org/10.5194/acp-22-1293-2022
Atmos. Chem. Phys., 22, 1293–1309, 2022
https://doi.org/10.5194/acp-22-1293-2022

Research article 25 Jan 2022

Research article | 25 Jan 2022

New particle formation event detection with Mask R-CNN

Peifeng Su et al.

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Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on acp-2021-771', Anonymous Referee #1, 23 Oct 2021
  • RC2: 'Comment on acp-2021-771', Anonymous Referee #2, 06 Nov 2021
  • AC1: 'Final response on acp-2021-771', Peifeng Su, 14 Dec 2021

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Peifeng Su on behalf of the Authors (14 Dec 2021)  Author's response    Author's tracked changes    Manuscript
ED: Publish as is (14 Dec 2021) by Manish Shrivastava
AR by Peifeng Su on behalf of the Authors (20 Dec 2021)  Author's response    Manuscript
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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.
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