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Related Experiment Video

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Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

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Underwater Acoustic Target Recognition Based on Depthwise Separable Convolution Neural Networks.

Gang Hu^1,2, Kejun Wang¹, Liangliang Liu¹

¹College of Automation, Harbin Engineering University, Harbin 150001, China.

Sensors (Basel, Switzerland)

|March 6, 2021

Summary

This summary is machine-generated.

This study introduces a novel deep neural network for underwater acoustic target recognition using ship noise. The model achieves superior feature extraction and recognition accuracy, improving performance by 6.8%.

Keywords:

deep learning depthwise separable convolution dilated convolution ship radiated noise underwater acoustic target

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Area of Science:

Marine acoustics
Signal processing
Artificial intelligence

Background:

Underwater acoustic target recognition is challenging due to complex marine environments.
Ship-radiated noise presents difficulties for accurate feature extraction and recognition.

Purpose of the Study:

To propose a novel deep neural network for underwater acoustic target recognition.
To enable automatic feature extraction from raw ship-radiated noise signals.
To improve recognition accuracy compared to traditional methods.

Main Methods:

A deep neural network model incorporating depthwise separable convolution and time-dilated convolution.
Utilizing one-dimensional time-domain raw ship signals as input.
Implementing temporal attention mechanisms for enhanced recognition.
Employing cluster and visualization analysis on extracted features.
Cross-validation to assess model generalization and prevent overfitting.

Main Results:

The proposed model successfully performs automatic feature extraction from raw ship-radiated noise.
Extracted features demonstrate good intra-class aggregation and inter-class separation.
The model shows no signs of overfitting, indicating strong generalization ability.
A significant accuracy improvement of 6.8% was achieved compared to traditional methods.

Conclusions:

The novel deep neural network effectively extracts features and recognizes underwater acoustic targets from ship noise.
The model's architecture and temporal attention enhance performance and generalization.
This approach offers a significant advancement in passive underwater acoustic target recognition.