Visualization Analysis and Kernel Pruning of Convolutional Neural Network for Ship-Radiated Noise Classification
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.
万方数据
维普
