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Title:Low-complexity convolutional neural networks for automatic target recognition
Author(s):Dbouk, Hassan
Advisor(s):Shanbhag, Naresh R
Department / Program:Electrical & Computer Eng
Discipline:Electrical & Computer Engr
Degree Granting Institution:University of Illinois at Urbana-Champaign
Degree:M.S.
Genre:Thesis
Subject(s):deep learning
neural networks
automatic target recognition
synthetic aperture radar
quantization
Abstract:Over the decades, several algorithms have been proposed for designing automatic target recognition systems based on synthetic aperture radar imagery. Recently, with the rise of Deep Learning, there has been growing interest in developing neural network based automatic target recognition systems for synthetic aperture radar applications. However, these networks are typically complex in terms of storage and computation which inhibits their deployment in the field, where such resources are heavily constrained. In order to reduce the cost of implementing these networks, in this thesis we develop a set of compact network architectures and train them in fixed-point. Our proposed method achieves an overall 984× reduction in terms of storage requirements and 71× reduction in terms of computational complexity compared to state-of-the-art convolutional neural networks for automatic target recognition, while maintaining a classification accuracy of >99% on the MSTAR dataset.
Issue Date:2020-03-11
Type:Thesis
URI:http://hdl.handle.net/2142/108091
Rights Information:Copyright 2020 Hassan Dbouk
Date Available in IDEALS:2020-08-26
Date Deposited:2020-05


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