Preprocessing performance of convolutional neural networks according to characteristic of underwater targets

Kyung-Min Park; Dooyoung Kim

doi:10.7776/ASK.2022.41.6.629

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2022 Vol.41, Issue 6 Preview Page Next Page

Research Article

Preprocessing performance of convolutional neural networks according to characteristic of underwater targets 수중 표적 분류를 위한 합성곱 신경망의 전처리 성능 비교

30 November 2022. pp. 629-636

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Abstract

We present a preprocessing method for an underwater target detection model based on a convolutional neural network. The acoustic characteristics of the ship show ambiguous expression due to the strong signal power of the low frequency. To solve this problem, we combine feature preprocessing methods with various feature scaling methods and spectrogram methods. Define a simple convolutional neural network model and train it to measure preprocessing performance. Through experiment, we found that the combination of log Mel-spectrogram and standardization and robust scaling methods gave the best classification performance.

Keywords

Underwater target

Convolution neural network

Preprocessing

Scaler

Outlier

본 논문은 합성곱 신경망 기반 수중 표적 분류기의 성능 향상을 위한 최적의 전처리 기법을 제시한다. 실제 선박 수중신호를 수집한 데이터 세트의 주파수 분석을 통해 강한 저주파 신호로 인한 특성 표현의 문제점을 확인하였다. 이를 해결하기 위해 다양한 스펙트로그램 기법과 특성 스케일링 기법을 조합한 전처리 기법들을 구현하였다. 최적의 전처리 기법을 확인하기 위해 실제 데이터를 기반으로 합성곱 신경망을 훈련하는 실험을 수행하였다. 실험 결과, 로그 멜 스펙트로그램과 표준화 및 로버스트정규화 스케일링 기법의 조합이 높은 인식 성능과 빠른 학습 속도를 보임을 확인하였다.

키워드

수중 표적

합성곱 신경망

전처리

스케일러

이상치

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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 : 41
No :6
Pages :629-636
Received Date : 2022-07-21
Revised Date : 2022-09-21
Accepted Date : 2022-10-05
DOI :https://doi.org/10.7776/ASK.2022.41.6.629

The Journal of the Acoustical Society of KoreaISSN:1225-4428(Print) 2287-3775(Online)한국음향학회

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