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2023 Vol.42, Issue 4 Preview Page

Research Article

Comprehensive analysis of deep learning-based target classifiers in small and imbalanced active sonar datasets

소량 및 불균형 능동소나 데이터세트에 대한 딥러닝 기반 표적식별기의 종합적인 분석

Geunhwan Kim1
,
Youngsang Hwang1
,
Sungjin Shin1
,
Juho Kim2
,
Soobok Hwang2
,
Youngmin Choo1*
김 근환1
,
황 용상1
,
신 성진1
,
김 주호2
,
황 수복2
,
추 영민1*
1세종대학교
2국방과학연구소
*Corresponding Author
31 July 2023. pp. 329-344
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Abstract

In this study, we comprehensively analyze the generalization performance of various deep learning-based active sonar target classifiers when applied to small and imbalanced active sonar datasets. To generate the active sonar datasets, we use data from two different oceanic experiments conducted at different times and ocean. Each sample in the active sonar datasets is a time-frequency domain image, which is extracted from audio signal of contact after the detection process. For the comprehensive analysis, we utilize 22 Convolutional Neural Networks (CNN) models. Two datasets are used as train/validation datasets and test datasets, alternatively. To calculate the variance in the output of the target classifiers, the train/validation/test datasets are repeated 10 times. Hyperparameters for training are optimized using Bayesian optimization. The results demonstrate that shallow CNN models show superior robustness and generalization performance compared to most of deep CNN models. The results from this paper can serve as a valuable reference for future research directions in deep learning-based active sonar target classification.

Keywords
Active sonar target classification
Deep learning
Imbalanced datasets
Bayesian optimization

본 논문에서는 소량 및 불균형 능동소나 데이터세트에 적용된 다양한 딥러닝 기반 표적식별기의 일반화 성능을 종합적으로 분석하였다. 서로 다른 시간과 해역에서 수집된 능동소나 실험 데이터를 이용하여 두 가지 능동소나 데이터세트를 생성하였다. 데이터세트의 각 샘플은 탐지 처리 이후 탐지된 오디오 신호로부터 추출된 시간-주파수 영역 이미지이다. 표적식별기의 신경망 모델은 다양한 구조를 가지는 22개의 Convolutional Neural Networks(CNN) 모델을 사용하였다. 실험에서 두 가지 데이터세트는 학습/검증 데이터세트와 테스트 데이터세트로 번갈아 가며 사용되었으며, 표적식별기 출력의 변동성을 계산하기 위해 학습/검증/테스트를 10번 반복하고 표적식별 성능을 분석하였다. 이때 학습을 위한 초매개변수는 베이지안 최적화를 이용하여 최적화하였다. 실험 결과 본 논문에서 설계한 얕은 층을 가지는 CNN 모델이 대부분의 깊은 층을 가지는 CNN 모델보다 견실하면서 우수한 일반화 성능을 가지는 것을 확인하였다. 본 논문은 향후 딥러닝 기반 능동소나 표적식별 연구에 대한 방향성을 설정할 때 유용하게 사용될 수 있다.

키워드
능동소나 표적식별
딥러닝
불균형 데이터세트
베이지안 최적화
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Information
  • Publisher :The Acoustical Society of Korea
  • Publisher(Ko) :한국음향학회
  • Journal Title :The Journal of the Acoustical Society of Korea
  • Journal Title(Ko) :한국음향학회지
  • Volume : 42
  • No :4
  • Pages :329-344
  • Received Date : 2023-06-09
  • Accepted Date : 2023-06-24
  • DOI :https://doi.org/10.7776/ASK.2023.42.4.329
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