TOPSIS method applied to the choice of the ideal object detection algorithm for an educational mobile application
André de Jesus Araújo Ramos,
Marcos César da Rocha Seruffo and
Roberto Célio Limão de Oliveira
International Journal of Management and Decision Making, 2024, vol. 23, issue 2, 252-264
Abstract:
This paper performs a literature review of the main methods of detection and classification of objects in real time, extracting the main characteristics that may influence their performance in smartphones. The main objective is, through the TOPSIS method, to choose the most suitable algorithm to later apply it in the task of detecting geometric shapes in streaming, from devices itself, efficiently, considering the limitations of such apparatus. This investigation contributes to making the best decision and, in subsequent works, it is intended to experimentally attest to the efficiency of such a choice in this objective. The adopted multi-criteria method indicated the YOLOv3 algorithm as the most suitable for this purpose and placed the faster-RCNN as the least ideal solution. This work brings a useful contribution to academia, as it presents a well-founded suggestion on how to popularise the use of this intelligence to object detection and classification in portable devices.
Keywords: multi-criteria; object detection algorithm; smartphone; TOPSIS; education. (search for similar items in EconPapers)
Date: 2024
References: Add references at CitEc
Citations:
Downloads: (external link)
http://www.inderscience.com/link.php?id=137075 (text/html)
Access to full text is restricted to subscribers.
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:ids:ijmdma:v:23:y:2024:i:2:p:252-264
Access Statistics for this article
More articles in International Journal of Management and Decision Making from Inderscience Enterprises Ltd
Bibliographic data for series maintained by Sarah Parker ().