A Novel Deep Learning Method for Underwater Target Recognition Based on Res-Dense Convolutional Neural Network with Attention Mechanism

Abstract

Long-range underwater targets must be accurately and quickly identified for both defense and civil purposes. However, the performance of an underwater acoustic target recognition (UATR) system can be significantly affected by factors such as lack of data and ship working conditions. As the marine environment is very complex, UATR relies heavily on feature engineering, and manually extracted features are occasionally ineffective in the statistical model. In this paper, an end-to-end model of UATR based on a convolutional neural network and attention mechanism is proposed. Using raw time domain data as input, the network model combines residual neural networks and densely connected convolutional neural networks to take full advantage of both. Based on this, a channel attention mechanism and a temporal attention mechanism are added to extract the information in the channel dimension and the temporal dimension. After testing the measured four types of ship-radiated noise dataset in experiments, the results show that the proposed method achieves the highest correct recognition rate of 97.69% under different working conditions and outperforms other deep learning methods.

Underwater Acoustic Target Recognition in Passive Sonar Using Spectrogram and Modified MobileNet Network Classifier

“…Also, the accuracy of the proposed method for underwater acoustic target recognition compared with other introduced methods in Table 6. Wavelet Transform (Average Power Spectral Density) [10] 92.64% CNN+k-nearest neighbor Wavelet Packets [11] 90.30% Deep Convolutional Neural Network Synthetic Aperture Sonar Imagery [13] 90.89% Support Vector Machine (SVM) Competitive Deep-Belief Networks [14] 93.04% DCGAN + S-ResNet Spectrum Image [15] 83.15% Multi-Scale Residual Unit (MSRU) Spectrogram [16] 90.91% Separable Convolutional Neural Network Waveform [17] 95.22% Convolutional Neural Network Low-Frequency Analysis Recording (LOFAR) [18] 98.52% Support Vector Machine (SVM) micro-Doppler sonar [19] 94.31% ResNet-18 Fusion Features [20] 96.32% ResNet Multi-Window Spectral Analysis (MWSA) [21] 97.69% ResNet and DensNet Spectrogram [22] 94.00% Convolutional Neural Network DEMON and LOFAR [23] 97.00% Bidirectional Short-Term Memory (Bi-LSTM) Time-Frequency Diagrams [24] 96.90% Convolutional Neural Network Acoustic Spectrograms [25] 98 As shown in the table above, compared to the existing methods for performing UATR, the proposed models have high classification accuracy, which can increase the processing speed of target recognition and avoid wasting time in model training calculation operations. In addition, in this research, due to the use of the average integration method at the end of the layers of the convolutional algorithms of the proposed model, instead of the fully connected layer, it has been tried to reduce the complexity and increase calculations.…”

“…The accuracy of recognition obtained in this method is 96.32%. Zeng et al [22] introduced a new model by integrating ResNet and DenseNet neural networks, which was able to classify targets with 97.69% recognition accuracy. Song et al [23] by combining the lowfrequency analysis recording (LOFAR) and Envelope modulation on noise (DEMON) and CNN network have been able to achieve 94.00% recognition accuracy.…”

Cross-Domain Contrastive Learning-Based Few-Shot Underwater Acoustic Target Recognition

“…Yang et al [12] designed a lightweight squeezing and residual network under a ResNet architecture to ensure recognition accuracy while compressing the model. Jin et al [13] utilize raw time-domain data as input to the model and incorporate an attention mechanism in a convolutional neural network to identify different types of ships. Inspired by visual transformers, Li et al [14] incorporated transformers into UATR for the first time, comparing the performance of three features: short-time Fourier transform (STFT), filter bank (FBank), and mel-frequency cepstrum coefficients (MFCCs).…”

Transformer Architecture and Attention Mechanisms in Genome Data Analysis: A Comprehensive Review

“…The attention mechanism, first introduced in sequence-to-sequence models [11], has revolutionized how deep learning models handle and interpret data [12][13][14][15][16][17][18]. This technique was designed to circumvent the limitations of traditional recurrent models by providing a mechanism to attend to different parts of the input sequence when generating the output.…”