Research On Underwater Static Target Recognition Method Based On Deep Convolution Feature

With the development of science and the abundance of materials,population,resources and environment become three major problems that humans are facing.For the sustainable development and long-term survival of humans,more and more attention is paid to ocean development.As the ocean is part of the country,marine security is increasingly challenged.Underwater target recognition technology that is a supporting technology for marine development and defense has attracted more and more attention.Underwater target recognition operation is an indispensable link of realizing underwater intelligence tasks,which acts as a role of human eyes.The paper aims to study the underwater target recognition technology based on deep convolution neural network,which includes the following research contents:1.For the characteristics of underwater optical image with high noise and low contrast,various classical underwater image filtering and local enhancement techniques are analyzed.Firstly,in order to reduce the influence of image preprocessing time on target recognition and improve the efficiency of target recognition,the paper proposes a median filtering technology based on set consistency decomposition acceleration.At the same time,combined with mean filtering technology,the paper proposes a fast mean-median filtering technology,which can reduce mixed noise of optical image effectively.Then,for the characteristics of low contrast of underwater image,this paper compares various local enhancement methods and selects the most suitable local enhancement method for multiple underwater optical image.2.In order to design and implement underwater deep convolution network for underwater static target recognition,at first,the design idea of convolution network and the classical structure that can be used for reference are analyzed in detail.And then,combining the characteristics of most underwater targets,the bottleneck problem of applying convolutional neural network directly to underwater target recognition is analyzed in this paper,that is,the contradictory relationship between deep network and small sample targets.3.The deep neural network is introduced into underwater optical image recognition task.Firstly,an underwater intelligent recognition framework is established.Then,an underwater end-to-end general identification network model is designed through transfer learning strategy.Next,the principle and characteristics of parameter selection in deep network learning are further analyzed,and finally,the paper selects appropriate network hyper-parameters and optimization methods to reduce the over-fitting effects of the model.4.The underwater image preprocessing is analyzed first,and then the underwater static target recognition simulation experiment is designed progressively from three levels: traditional convolution,deep convolution,and deep convolution based on transfer learning strategy.Finally,the paper realizes the underwater static target recognition based on deep convolution feature and compares the recognition effect of various methods,which provides practical reference for future academic research and engineering application.In this paper,the underwater target recognition technology based on deep convolution neural network is studied,which includes filtering and local enhancement preprocessing of underwater optical image,establishment of underwater target intelligent recognition framework,design of an underwater end-to-end general deep convolution neural network based on transfer learning strategy.At the same time,data enhancement technology is introduced,and finally,the paper realizes underwater static target recognition based on deep convolution neural network and compares with traditional recognition methods.