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Название: Research of methods to increase the efficiency of the object detection system on the Raspberry Pi platform
Авторы: Koshutina, Daria
Кошутіна, Дар’я Валеріївна
Antoshchuk, Svitlana
Антощук, Світлана Григорівна
Ключевые слова: robotics
Raspberry Pi
Python
machine learning
deep learning
convolutional neural networks
computer vision
object detection
Single-Shot MultiBox Detector
R-CNN
Дата публикации: 2023
Издательство: Anhalt University of Applied Sciences
Библиографическое описание: Koshutina, D., Antoshchuk, S. (2023). Research of methods to increase the efficiency of the object detection system on the Raspberry Pi platform. Proceedings of International Conference on Applied Innovation in IT, Volume 11, Issue 1, P. 127-132.
Краткий осмотр (реферат): 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 (Унифицированный идентификатор ресурса): http://dspace.opu.ua/jspui/handle/123456789/15453
ISSN: 21998876
Располагается в коллекциях:2023

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