Open AccessComparative Analysis of Deep Neural Networks Architectures for Visual Recognition in the Autonomous Transport Systems
Author(s) -
Yuriy Ivanov,
S.V. Zhiganov,
N N Liubushkina
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2096/1/012101
Subject(s) - artificial intelligence , computer science , robotics , cognitive neuroscience of visual object recognition , transformer , artificial neural network , deep neural networks , deep learning , machine learning , robot , object (grammar) , engineering , voltage , electrical engineering
This paper analyses and presents an experimental investigation of the efficiency of modern models for object recognition in computer vision systems of robotic complexes. In this article, the applicability of transformers for experimental classification problems has been investigated. The comparison results are presented taking into account various limitations specific to robotics. Based on the results of the undertaken studies, recommendations on the use of models in the marine vessels classification problem are proposed