Underwater Target Recognition Method Based on Deep Convolution Feature

With the help of artificial intelligence technology, the intelligent recognition technology for unmanned undersea vehicle(UUV) to detect underwater target is presented. According to the characteristics of high noise and low contrast in underwater environment, median filtering and local enhancement are adopted to preprocess underwater images. Based on the characteristics of small sample of underwater images, the idea of incremental convolution layer by layer is proposed drawing from the visual geometry group net(VGGNet), and an underwater intelligent recognition framework is designed by using deep convolution neural network (DCNN) for training on the large data set Cifar-10 and learning general features of the image. In addition, migration learning and data enhancement technology are used for transferring knowledge to learn the unique features of underwater targets, so as to solve the problem of insufficient underwater data sets and to prevent over-fitting. Simulation results show that the proposed underwater target intelligent recognition technology has obvious advantages over the traditional algorithm in recognition effect and automation degree on specific test set.