Object classification with artificial neural networks : A comparative analysis

• Autorzy: Domeradzki Kornel , Niewiadomski Artur

Identyfikatory

DOI

10.34739/si.2019.23.03

• Języki publikacji: EN

Abstrakty

EN

Object classification is a problem which has attracted a lot of research attention in recent years. Traditional approach to this problem is built on a shallow trainable architecture that was meant to detect handcrafted features. That approach works poorly and introduces many complications in situations where one is to work with more than a couple types of objects in an image with a large resolution. That is why in the past few years convolutional and residual neural networks have experienced a tremendous rise in popularity. In this paper, we provide a review on topics related to artificial neural networks and a brief overview of our research. Our review begins with a short introduction to the topic of computer vision. Afterwards we cover briefly the concepts of neural networks, convolutional and residual neural networks and their commonly used models. Then we provide a comparative performance analysis of the previously mentioned models in a binary and multi-label classification problem. Finally, multiple conclusions are drawn, which are to serve as guidelines for future computer vision systems implementations.

Słowa kluczowe

EN

object classification; neural networks; convolutional neural networks; residual neural networks

• Wydawca: Wydawnictwo Uniwersytetu Przyrodniczo-Humanistycznego w Siedlcach
• Czasopismo: Studia Informatica : systems and information technology
• Rocznik: 2019
• Tom: Vol. 1-2(23)
• Strony: 43--56
• Opis fizyczny: Bibliogr. 14 poz., rys., wykr.

Twórcy

autor

Domeradzki Kornel

• Siedlce University of Natural Sciences and Humanities, Faculty of Exact and Natural Sciences, Institute of Computer Science, ul. 3 Maja 54, 08-110 Siedlce, Poland

autor

Niewiadomski Artur

• Siedlce University of Natural Sciences and Humanities, Faculty of Exact and Natural Sciences, Institute of Computer Science, ul. 3 Maja 54, 08-110 Siedlce, Poland

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