Open AccessDeep Learning Based Classification of Radar Spectral Maps
Author(s) -
Tong Lin,
Xin Chen,
Xiao Tang,
Ling He,
Song He,
Qiaolin Hu
Publication year - 2021
Publication title -
international journal of electrical and electronic engineering and telecommunications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.171
H-Index - 6
ISSN - 2319-2518
DOI - 10.18178/ijeetc.10.2.99-104
Subject(s) - artificial intelligence , computer science , convolutional neural network , radar , deep learning , pattern recognition (psychology) , contextual image classification , radar imaging , artificial neural network , machine learning , image (mathematics) , telecommunications
This paper discusses the use of deep convolutional neural networks for radar target classification. In this paper, three parts of the work are carried out: firstly, effective data enhancement methods are used to augment the dataset and address unbalanced datasets. Second, using deep learning techniques, we explore an effective framework for classifying and identifying targets based on radar spectral map data. By using data enhancement and the framework, we achieved an overall classification accuracy of 0.946. In the end, we researched the automatic annotation of image ROI (region of interest). By adjusting the model, we obtained a 93% accuracy in automatic labeling and classification of targets for both car and cyclist categories.