Proposal of Depth Estimation Method Using Deep Learning for Droplet Visualization

In the Urea-SCR system, two-dimensional visualization images of spray droplet taken by a high-speed camera are used for verification of atomization and measurement of droplet diameter. This paper describes the method for predicting the droplet diameter from visualization image with depth of field using deep learning to improve accuracy of measuring droplet size distribution. As a result from trained model, this method showed applicability to measure droplet within various depths.

• Record URL:
  https://doi.org/10.11351/jsaeronbun.52.1071
• Record URL:
  https://www.jstage.jst.go.jp/article/jsaeronbun/52/5/52_20214728/_pdf
• 
• Record URL:
  https://scholar.google.com/scholar_lookup?title=Proposal%20of%20Depth%20Estimation%20Method%20Using%20Deep%20Learning%20for%20Droplet%20Visualization&author=A.%20Obara&author=A.%20Kikuchi&author=Y.%20Kawamoto&author=N.%20Sugiyama&author=Y.%20Kuramoto&author=S.%20Nara&author=M.%20Ochiai&author=S.%20Takahashi&author=T.%20Nohara&publication_year=2021
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/02878321
• Authors:
  • Obara, Akira
  • Kikuchi, Asuka
  • Kawamoto, Yuki
  • Sugiyama, Naoki
  • Kuramoto, Yuiki
  • Nara, Shotaro
  • Ochiai, Masayuki
  • Takahashi, Shun
  • Nohara, Tetsuo
• Publication Date: 2021-9

Language

• English
• Japanese

Media Info

• Media Type: Digital/other
• Features: Figures; References; Tables;
• Pagination: pp 1071-1076
• Serial:
  • Transactions of Society of Automotive Engineers of Japan
  • Volume: 52
  • Issue Number: 5
  • Publisher: Society of Automotive Engineers of Japan
  • ISSN: 0287-8321
  • EISSN: 1883-0811
  • Serial URL: https://www.jstage.jst.go.jp/browse/jsaeronbun
• Publication flags:

  Open Access (libre)

Subject/Index Terms

• TRT Terms: Diameter; Estimating; High speed photography; Machine learning; Selective catalytic reduction; Visualization
• Subject Areas: Energy; Highways; Vehicles and Equipment;

Filing Info

• Accession Number: 01787952
• Record Type: Publication
• Source Agency: Japan Science and Technology Agency (JST)
• Files: TRIS, JSTAGE
• Created Date: Nov 12 2021 5:23PM