Underwater Acoustic Target Recognition Based on Depthwise Separable Convolution Neural Networks

Abstract

Facing the complex marine environment, it is extremely challenging to conduct underwater acoustic target feature extraction and recognition using ship-radiated noise. In this paper, firstly, taking the one-dimensional time-domain raw signal of the ship as the input of the model, a new deep neural network model for underwater target recognition is proposed. Depthwise separable convolution and time-dilated convolution are used for passive underwater acoustic target recognition for the first time. The proposed model realizes automatic feature extraction from the raw data of ship radiated noise and temporal attention in the process of underwater target recognition. Secondly, the measured data are used to evaluate the model, and cluster analysis and visualization analysis are performed based on the features extracted from the model. The results show that the features extracted from the model have good characteristics of intra-class aggregation and inter-class separation. Furthermore, the cross-folding model is used to verify that there is no overfitting in the model, which improves the generalization ability of the model. Finally, the model is compared with traditional underwater acoustic target recognition, and its accuracy is significantly improved by 6.8%.

The Cognitive Transformation of Japanese Language Education by Artificial Intelligence Technology in the Wireless Network Environment

“…Object detection models based on CNNs are classified into the one-stage type based on region proposal and the two-stage type based on regression [ 10 , 11 ]. The one-stage type based on region proposal directly uses the forward neural network to predict the prediction box of interest.…”

Underwater Target Signal Classification Using the Hybrid Routing Neural Network

“…In Figure 9 , the used network is compared with DNN [ 13 ], ADCNN [ 15 ], DepthCNN [ 16 ], CDBN [ 17 ], and CRNN [ 18 ]. HRNet is the hybrid routing network method.…”

“…The original signal data are input into the depthwise separable CNN (DSCNN) in the temporal domain. The desired identification effects suggest that the model inherits from the function of intra-class concentration, which isolates inter-class characteristics at the same time [ 16 ]. The deep-belief network utilizes the pre-processing approach of standard Boltzmann machine, the hidden middle layer of the clustering method, and the training optimization of parameter updating [ 17 ].…”

Securing IoT Devices: A Robust and Efficient Deep Learning with a Mixed Batch Adversarial Generation Process for CAPTCHA Security Verification

“…The aforementioned observation was proven by several researchers [3][4][5][6][7][8][9][10][11][12][13][14]. DL is beneficial in other fields, including target recognition [15], speech recognition [16,17], image recognition [18][19][20], image restoration [21][22][23], audio classification [24,25], object detection [26][27][28][29][30], scene recognition [31], etc., but it has been considered "bad news" in text-based CAPTCHAs, by penetrating their security and making them vulnerable.…”