Research On Ocean Target Recognition Method Based On Deep Learning

With the continuous development of deep learning,deep learning has been widely used in image,speech,natural language processing,and big data feature extraction.After more and more fields are integrated with deep learning technology,tasks that were originally difficult to handle are easier.The recognition and classification of marine targets has always been an important content of research in the field of underwater acoustic signal processing.However,traditional methods consume large amounts of resources.Therefore,in the recognition of marine targets,more and more researchers use deep learning methods.This paper mainly studies the marine target recognition method based on deep learning.This article combines deep learning with the field of marine target recognition,and achieves the purpose of recognition and classification by preprocessing and feature extraction of marine targets.First,ocean audio data is collected through various channels,and after filtering and collating,they are aggregated into experimental data sets.Because the classification of audio files is not effective,this article attempts to convert the marine target into several spectrums and then classify them,so as to improve the recognition rate.Experimental results show that this method greatly improves the recognition accuracy.Through experiments,it is found that the LOFAR spectrum has the highest accuracy among multiple spectrum comparisons,that is,the LOFAR spectrum is the most suitable spectrum for deep learning training in the four types of spectrograms selected in this paper.At the same time,based on the convolutional neural network,this paper designs a network structure suitable for LOFAR samples,which greatly improves the recognition accuracy,the accuracy rate reaches 90.3%,and achieves the expected target that can be practically applied.Secondly,because there are few datasets of marine targets based on deep learning,and the size of the dataset plays a crucial role in the model training effect of the deep neural network framework,this paper designs a generative adversarial network suitable for the LOFAR spectrum.The underwater acoustic data set is expanded to further improve the classification and recognition ability of deep learning in the field of marine target processing,with an accuracy rate of 95.6%.Finally,in practical applications,there is a high requirement for the recognition rate,so this paper designs a lightweight neural network for identifying the LOFAR spectrum of embedded devices with limited resources.The experimental results show that the amount of parameters has decreased by 14%.The calculation volume has decreased by 91.3%,and the calculation speed has been improved.In this paper,deep learning technology is applied in the field of marine target processing,preprocessing and feature extraction of marine targets,and the use of generative adversarial networks to expand the data set to achieve the purpose of identification and classification,and the use of lightweight neural networks reduces The volume of the model improves the recognition speed and reaches the standard that can be used on embedded devices with limited resources.