합성곱 순환 신경망 모델을 이용한 의사 레이블링 기법 기반 능동소나 표적 식별 약지도 딥러닝 알고리즘 연구

In this paper, we proposed the weakly-supervised deep learning algorithm for active sonar target recognition based on pseudo labeling using Conventional Recurrent Neural Network (CRNN) model widely used for acoustic signal processing because it can effectively utilize small and unbalanced active sonar data. Active sonar simulation data assuming two different SNRs and clutter environments were used in the training and testing process, and spectrogram obtained by applying Short Time Fourier Transform (STFT) to the simulation data was used as a feature factor for algorithm training. The algorithm proposed in this paper was evaluated based on the target and nontarget F1-score using test data independent of training data. As a result, it was confirmed that the CRNN model showed significant performance not only in typical acoustic signal processing but also active sonar target recognition. Also, pseudo-labeling helps to improve the performance of the active sonar target recognition algorithm used the CRNN model.