Research On Target Sample Generation And Classification Of Side Scan Sonar Image

With the continuous improvement of sonar systems and the emergence of underwater intelligent robots,research in the area of underwater target recognition and classification is also continuously deepening.The classification of underwater sonar images in the civilian and military fields has been widely used,and it is helpful to find submarines and crashed aircraft in the military.For civilian use,it is helpful to find fish schools and detect the integrity of the dam bottom.Traditional underwater target classification methods mainly use feature extraction methods for classification,but such methods often have the disadvantage of high application limitations.This article analyzes the principles of sonar imaging and the characteristics of sonar images,uses deep learning and transfer learning to classify sonar images,and according to the characteristics of deep learning requiring a large number of samples,transfer learning methods are compared with deep neural networks.In combination,according to the basic principle of transfer learning methods,the closer the field distance is,the more conducive to the transfer of knowledge.The method of graphic style transfer and the generation of adversarial networks is used to generate a pseudo-side-scan sonar image similar to the side-scan sonar image.Source domain,and then use a small number of real samples to migrate,so as to achieve the purpose of high-accuracy side-scan sonar image target classification.The specific work of the paper is as follows:(1)The traditional target classification method is studied.Firstly,the characteristics of the sonar image are analyzed.It is learned that the sonar image generally has the problems of poor definition,low resolution,and serious noise.The traditional underwater sonar image classification method mainly focuses on the characteristics of the sonar image.The conventional optical image method is improved,mainly including several steps such as preprocessing,feature extraction,feature classification,etc.The experiments and analysis on the denoising of the sonar image and the gray correction wind are carried out to further improve the image quality.Gauss-Markov random field and level set methods were used to extract image features,and the images were classified based on the extracted features.(2)Aiming at the characteristics of sonar images and the shortcomings of traditional methods,a deep learning method is used to automatically extract the features of the images,and the classic convolutional neural network is analyzed and compared.At the same time,the method of transfer learning is used to analyze The influence of the distance between domains on the transfer efficiency is proposed,and a transfer learning method based on reducing the domain distance of the sample is proposed.(3)Aiming at the sonar-like processing of source domain samples,a conversion method is proposed which combines saliency detection and style transfer network.Methods The conventional optical image was converted into a side-scan sonar image style through a style transfer network,and the images with poor conversion effects were processed.The saliency detection algorithm was mainly used.This algorithm was combined with the style transfer method to solve the problem.In order to solve the problem of poor style transfer caused by too high background brightness,the imitation sonar image generated by the style transfer method is used as the source domain sample to train the classification network,and then transfer learning method is used to migrate it to the real side scan.In the sonar image sample classification task,the experimental analysis conclusion is finally given.(4)At the same time,for the simulation of sonarization in the source domain samples,an improved method for generating side scan sonar images based on improved WGAN is also proposed,which does not rely on conventional optical images and directly learns the pixel distribution in the real side scan sonar images.In order to generate images,due to the small number of samples in the side-scan sonar image data set,there may be large deviations in the data distribution.Therefore,when directly applying WGAN,there may be a problem of poor authenticity of the generated images due to improper parameter settings.The regular term is integrated into the loss function of WGAN,and the parameter range is automatically determined to ensure the image generation quality.The migration experiment proves the effectiveness of this method.