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http://dx.doi.org/10.6109/jkiice.2017.21.10.1909

Target Classification of Active Sonar Returns based on Convolutional Neural Network  

Kim, Jeong-Hun (LIG Nex1 Co., Ltd.)
Choi, Dae-Sung (LIG Nex1 Co., Ltd.)
Lee, Hyung-Soo (LIG Nex1 Co., Ltd.)
Lee, Jung-Woo (LIG Nex1 Co., Ltd.)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.21, no.10, 2017 , pp. 1909-1916 More about this Journal
Abstract
Recently, deep learning algorithms have good performance in various fields, but they are not actively applied to sonar systems. In this study, we carried out experiments to classify active sonar returns into a metal object such as a mine and a rock using a convolutional neural network which is one of the deep learning algorithms. Data augmentation is applied on this paper to avoid overfitting and increase performance. And we analyzed performance variation depending on hyperparameter value and change of the number of training data through data augmentation. The experiments are performed with two training data; an aspect-angle independent and an aspect-angle dependent. As a result, the performances are 88.9% and 94.9% in aspect-angle independent and dependent, respectively. These are up to 4.5% point higher than the performance obtained by applying artificial neural network and support vector machine algorithm in the previous study.
Keywords
Active Sonar; Deep Learning; Convolutional Neural Network; Mine;
Citations & Related Records
Times Cited By KSCI : 4  (Citation Analysis)
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