Research of YOLO Architecture Models in Book Detection

DOI

10.2991/aisr.k.201029.042How to use a DOI?

Keywords

image recognition, object detection, computer vision, machine learning, artificial neural networks, deep learning, convolutional neural networks

Abstract

Deep neural networks are widely used in different fields of human activity, including spheres which are connected with large amount of operations such as data obtaining and processing information from the outside world. This article deals with the creation of the deep convolutional neural network based on the YOLO architecture for book detection in real time. The architecture chosen as the basis of the neural network possesses a number of advantages which make it highly competitive with other models, so it can be considered as the most suitable option for the creation of deep neural network for object detection. Creation of the original dataset and the deep neural network training are described. Several variants of neural networks based on the YOLO architecture are discussed and the results of their comparison are shown. The results obtained during the training of a deep neural network allow us to use it as a basis for further development of the application.

Copyright

© 2020, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Maria Kalinina
AU  - Pavel Nikolaev
PY  - 2020
DA  - 2020/11/10
TI  - Research of YOLO Architecture Models in Book Detection
BT  - Proceedings of the 8th Scientific Conference on Information Technologies for Intelligent Decision Making Support (ITIDS 2020)
PB  - Atlantis Press
SP  - 218
EP  - 221
SN  - 1951-6851
UR  - https://doi.org/10.2991/aisr.k.201029.042
DO  - 10.2991/aisr.k.201029.042
ID  - Kalinina2020
ER  -