Analysis of algorithms for searching objects in images using convolutional neural network

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dc.citation.epage133
dc.citation.issueVolume 6, № 2
dc.citation.journalTitleAdvances in Cyber-Physical Systems
dc.citation.spage128
dc.contributor.affiliationLviv Polytechnic National University
dc.contributor.authorKoval , Ihor
dc.date.accessioned2022-12-01T09:42:56Z
dc.date.available2022-12-01T09:42:56Z
dc.date.issued2021
dc.date.submitted2022
dc.description.abstractThe problem of finding objects in images using modern computer vision algorithms has been considered. The description of the main types of algorithms and methods for finding objects based on the use of convolutional neural networks has been given. A comparative analysis and modeling of neural network algorithms to solve the problem of finding objects in images has been conducted. The results of testing neural network models with different architectures on data sets VOC2012 and COCO have been presented. The results of the study of the accuracy of recognition depending on different hyperparameters of learning have been analyzed. The change in the value of the time of determining the location of the object depending on the different architectures of the neural network has been investigated.
dc.format.pages128-133
dc.identifier.citationKoval I. Analysis of algorithms for searching objects in images using convolutional neural network / Ihor Koval // Advances in Cyber-Physical Systems. – Lviv : Lviv Politechnic Publishing House, 2021. – Volume 6, № 2. – P. 128–133 . – Bibliography: 17 titles.
dc.identifier.doihttps://doi.org/10.23939/acps2021.02.128
dc.identifier.urihttps://ena.lpnu.ua/handle/ntb/57243
dc.language.isoen
dc.publisherLviv Politechnic Publishing House
dc.relation.ispartofAdvances in Cyber-Physical Systems
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dc.subjectObject detection, neural networks, deep learning, R-CNN, Fast R-CNN, Faster R-CNN
dc.titleAnalysis of algorithms for searching objects in images using convolutional neural network
dc.typeArticle

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Advances In Cyber-Physical Systems. – 2021. – Vol. 6, No. 2