Deep learning-based adaptive weight convolutional neural network underwater sonar image classification method
A technology of convolutional neural network and self-adaptive weight, which is applied in the direction of biological neural network model, neural architecture, instrument, etc., can solve the complex seabed situation, increase the difficulty of underwater sonar image classification, and cannot take underwater acoustics into account. problem of accepting useful information of images, etc.
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[0081] The present invention is described in more detail below in conjunction with accompanying drawing example:
[0082] combine figure 1 , the concrete steps of the present invention are as follows:
[0083] (1) Generate DBN two-dimensional parameter matrix
[0084] The underwater sonar images belong to the small-sample undisclosed data set. The experimental data set of the present invention comes from laboratory collection and collection over the years. The data set is divided into six categories, including underwater sand patterns, sunken ships, sunken planes, stones, tires and school of fish. Due to the obvious advantages of deep learning in big data, the present invention considers various situations of underwater sonar images, such as image angle inclination, noise, etc., and expands the data set. These include H, hv, S, V channel conversion, R, G, B single channel conversion, image flip operation, in order to simulate the complex environment of the seabed, Gaussian ...
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