MATEC Web Conf.
Volume 283, 2019The 2nd Franco-Chinese Acoustic Conference (FCAC 2018)
|Number of page(s)||5|
|Published online||28 June 2019|
Underwater target recognition method based on convolution residual network
Science and Technology on Sonar Laboratory, Hangzhou Applied Acoustics Research Institute, Hangzhou, 310023, China
* Corresponding author: email@example.com
The underwater target radiated noises usually have characteristics of low signal to noise ratio, complex signal components and so on. Therefore the recognition is a difficult task and powerful recognition method must be applied to obtain good results. In this paper, a recognition method for underwater target radiated noise time-frequency image based on convolutional neural network with residual units is proposed. The principles and characteristics of the convolutional residual network are analyzed and three basic convolutional residual units are put forward. Then three convolutional residual network models with very deep structure are established based on basic convolutional residual units and some normal convolution layers. The number of the hidden layers is 50, 100 and 150 respectively and softmax algorithm is used as the top classifier. The wavelet transform is adopted to generate time-frequency images of the underwater target radiated noises with frequency band of 10~200Hz, thus ensuring the accuracy of local structure of the image, then the above three models can be used to recognize the images. The experimental data of two types of targets were processed. The results are as follows. As the number of training time increases, the training loss shows a convergence trend and the recognition accuracy of test data gradually increases to more than 90%. In addition, the top-level output has obvious separability. The final recognition accuracies of the three convolutional residual networks are all over 93% and higher than that of normal convolutional neural network with 5 layers. As the number of layers increases, the recognition accuracy of the convolutional residual network increases to a certain extent, illustrating the increase of layer number can improve the processing effect. The analysis results show that the convolution residual network can extract features with separability through deep structure and achieve effective underwater target recognition.
© The Authors, published by EDP Sciences, 2019
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