JOURNAL ARTICLE

Development of Software Module for Recognizing Traffic Flows Through Deep Learning.

  • Published In: Journal of Industrial Integration & Management, 2023, v. 8, n. 2. P. 175 1 of 3

  • Database: Business Source Ultimate 2 of 3

  • Authored By: Gorodnichev, Mikhail G; Erokhin, Sergey D; Sheremetev, Alexander V; Gematudinov, Rinat A; Moseva, Marina S 3 of 3

Abstract

This work describes a software system that uses machine learning methods and classifies vehicles observed in a frame. Analysis of the subject area and the possibilities provided by the methods of machine learning in the framework of the performance of assigned tasks are realized. An important part of the work is devoted to a study of the features of machine learning technologies with a teacher. In particular, the basic principles of the functioning of convolutional artificial neural networks are considered. This paper describes the implementation of the final software module, and its functionality, and demonstrates the results of its testing. Results from the developed software module show significant improvement in the accuracy over other methods, such as Artificial Neural Networks. Further developments of this technology may lead to broader applications and further improvements. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Journal of Industrial Integration & Management. 2023/06, Vol. 8, Issue 2, p175
  • Document Type:Article
  • Subject Area:Computer Science
  • Publication Date:2023
  • ISSN:2424-8622
  • DOI:10.1142/S2424862222500178
  • Accession Number:163910175
  • Copyright Statement:Copyright of Journal of Industrial Integration & Management is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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