Automated Object Recognition System based on Convolutional Autoencoder

This paper describes the model, implementation and the experimental verification of an aerial image processing and recognition technology based on artificial neural networks, specifically, convolutional autoencoders and classifying perceptrons. An originally developed model that includes autoencoder...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:2020 10th International Conference on Advanced Computer Information Technologies (ACIT) S. 830 - 833
Hauptverfasser: Prystavka, Pylyp, Cholyshkina, Olha, Dolgikh, Serge, Karpenko, Denys
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.09.2020
Schlagworte:
Artificial Neural Networks
Autoencoders
Buildings
Convolutional Neural Networks
Data visualization
Deep Learning
Feature extraction
Image recognition
Image segmentation
Machine Learning
Object recognition
Training
ISBN:1728167590, 9781728167596
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:This paper describes the model, implementation and the experimental verification of an aerial image processing and recognition technology based on artificial neural networks, specifically, convolutional autoencoders and classifying perceptrons. An originally developed model that includes autoencoder preprocessing for compression and extraction of informative features was applied to the task of pattern recognition, namely, locating and identifying the objects of certain higher-level classes of interest in the images produced by aerial photography. Classification efficiency of the method was measured and compared with other common methods of classification, the advantages and shortcomings of the proposed approach analyzed and potential applications in real-time remote object recognition systems, as well as in automating the generation of training data for image recognition systems discussed.
ISBN:1728167590
9781728167596
DOI:10.1109/ACIT49673.2020.9208945