Visualization Analysis and Kernel Pruning of Convolutional Neural Network for Ship-Radiated Noise Classification

XU Yuanchao; CAI Zhiming; KONG Xiaopeng; HUANG Yan

doi:10.11999/JEIT230149

Volume 46 Issue 1

Jan. 2024

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Article Navigation > Journal of Electronics & Information Technology > 2024 > 46(1): 74-82

XU Yuanchao, CAI Zhiming, KONG Xiaopeng, HUANG Yan. Visualization Analysis and Kernel Pruning of Convolutional Neural Network for Ship-Radiated Noise Classification[J]. Journal of Electronics & Information Technology, 2024, 46(1): 74-82. doi: 10.11999/JEIT230149

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XU Yuanchao, CAI Zhiming, KONG Xiaopeng, HUANG Yan. Visualization Analysis and Kernel Pruning of Convolutional Neural Network for Ship-Radiated Noise Classification[J]. Journal of Electronics & Information Technology, 2024, 46(1): 74-82. doi: 10.11999/JEIT230149

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Visualization Analysis and Kernel Pruning of Convolutional Neural Network for Ship-Radiated Noise Classification

doi: 10.11999/JEIT230149

College of Electronic Engineering, Naval University of Engineering, Wuhan 430033, China

Received Date: 2023-03-13
Rev Recd Date: 2023-06-12

Available Online: 2023-06-19

Publish Date: 2024-01-17

Abstract

Abstract

Current research on the classification of ship-radiated noise utilizing deep neural networks primarily focuses on aspects of classification performance and disregards model interpretation. To address this issue, an approach involving guided backwardpropagation and input space optimization has been utilized to develop a Convolutional Neural Network (CNN) for ship-radiated noise classification. This CNN takes a logarithmic scale spectrum as input and is based on the DeepShip dataset, thus presenting a visualization method for ship-radiated noise classification. Results reveal that the multiframe feature alignment algorithm enhances the visualization effect, and the deep convolutional kernel detects two types of features: line spectrum and background. Notably, the line spectrum has been identified as a reliable feature for ship classification. Therefore, a convolutional kernel pruning method has been proposed. This approach not only enhances the performance of CNN classification, but also enhances the stability of the training process. The results of the guided backwardpropagation visualization suggest that the post-pruning CNN increasingly emphasizes the consideration of line spectrum information.
- Ship-radiated noise classification,
- Convolutional Neural Network(CNN),
- Visualization analysis,
- Neural network pruning,
- Guided backward propagation

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References(19)

References

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