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http://dx.doi.org/10.25673/101928
Titel: | Research of Methods to Increase the Efficiency of the Object Detection System on the Raspberry Pi Platform |
Autor(en): | Koshutina, Daria Antoshchuk, Svetlana |
Körperschaft: | Hochschule Anhalt |
Erscheinungsdatum: | 2023 |
Umfang: | 1 Online-Ressource (6 Seiten) |
Sprache: | Englisch |
Zusammenfassung: | The work is devoted to the consideration and solution of the problems of object detection efficiency. This article analyzes object detection methods. The existing methods and systems of object detection are considered. On the basis of the researched methods, prospects and further directions for the development of object detection programs are defined. This research is relevant in today’s world, because smart devices, robots and robotic systems are increasingly being used to improve life. Therefore, the object detection system is an important part of robotics and automation. The development of a real-time object detection algorithm on the Raspberry Pi platform is described. The method of automatic detection and recognition of objects is described. To check the effectiveness of the methods, a system was designed and implemented, which is a camera connected to Raspberry Pi using the algorithm developed during the work. The problem consists in creating algorithms and methods to improve the response time and accuracy of object detection in real time. The system was created on the basis of already existing research results, refinement and implementation of the methods proposed in them In the course of the study, the results of the development of the object detection system based on the developed algorithms were presented and their effectiveness was investigated. |
URI: | https://opendata.uni-halle.de//handle/1981185920/103879 http://dx.doi.org/10.25673/101928 |
Open-Access: | Open-Access-Publikation |
Nutzungslizenz: | (CC BY 4.0) Creative Commons Namensnennung 4.0 International |
Enthalten in den Sammlungen: | International Conference on Applied Innovations in IT (ICAIIT) |
Dateien zu dieser Ressource:
Datei | Beschreibung | Größe | Format | |
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2_10 ICAIIT_2023_paper_8420.pdf | 1.11 MB | Adobe PDF | Öffnen/Anzeigen |