Recognition of nanoparticles on scanning probe microscopy images using computer vision and deep machine learning

Aleksey G. Okunev, Anna V. Nartova, Andrey V. Matveev

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

Abstract

Identifying and counting individual particles is an important component of many studies in various explorations. In the paper we present the results of the application of deep learning methods for the automated recognition of platinum nanoparticles deposited on highly oriented pyrolytic graphite (HOPG) on images obtained by scanning tunneling microscopy (STM). We used the neural network CascadeRCNN. The training was performed on a data set containing 10 STM images with 1918 nanoparticles. Five images containing 2052 nanoparticles were used for verification. As a result, the trained neural network recognized nanoparticles in verification set with 50.8% accuracy. Nanoparticles are specified as distinct contours, which are necessary for further determination of the particles dimensions (size, height etc). The obtained results were compared with the possibilities of other software products. The advantage of using deep machine learning methods for automatic particle recognition is clearly shown.

Original languageEnglish
Title of host publicationSIBIRCON 2019 - International Multi-Conference on Engineering, Computer and Information Sciences, Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages940-943
Number of pages4
ISBN (Electronic)9781728144016
DOIs
Publication statusPublished - Oct 2019
Event2019 International Multi-Conference on Engineering, Computer and Information Sciences, SIBIRCON 2019 - Novosibirsk, Russian Federation
Duration: 21 Oct 201927 Oct 2019

Publication series

NameSIBIRCON 2019 - International Multi-Conference on Engineering, Computer and Information Sciences, Proceedings

Conference

Conference2019 International Multi-Conference on Engineering, Computer and Information Sciences, SIBIRCON 2019
CountryRussian Federation
CityNovosibirsk
Period21.10.201927.10.2019

Keywords

  • deep neural networks
  • nanoparticles
  • particles recognition
  • scanning tunneling microscopy

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