Underwater target recognition method based on deep learning

Abstract

The present invention relates to an underwater target recognition method based on deep learning, which mainly solves the problem that the existing target recognition system mainly relies on the shallow model to extract the artificial feature, resulting in the recognition accuracy is not high. The method comprises the following concrete steps that (1) power spectrum characteristics of original ship target radiated noise data are obtained in segment; (2) the power spectrum characteristics are divided into a training data set and a testing data set; (3) training data are subjected to ZCA whitening pretreatment; (4) a stack-type self-coding network is constructed; (5) a deep network node is subjected to fine tuning; and (6) testing data are subjected to classification and recognition. Sea test data and experiments show that the deep network is utilized to learn deep characteristics of ship target radiated noise for performing classification recognition, the recognition rate reaches over 94%, and the underwater target recognition effect is improved, which has an important real application prospect for future recognition and monitoring of underwater ships and marine organisms.

Description

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AI Technical Summary

Benefits of technology

This new technology described in this patents allows targets such as fish or other organisms to be identified accurately without requiring human labor. It involves training an artificial neural network (ANN) that can extract relevant data patterns from their environment through various techniques like convolutional networks or generative adversarial nets. These models have been trained over time and tested against different scenarios - including sea water conditions or environmental factors affecting them. Overall, these technologies improve efficiency and accuracy in identifying objects within environments where they may harm humans.

Problems solved by technology

This patents discusses the challenges faced during active sea radar (active sonobuo) detection tasks where identifying objects accurately involves analyzing and studying both the sound pressure waves generated by the vessel itself and its surrounding environments with complex background noises like ocean sounds. Existing methodology relies heavily on manual inputting of specific signatures into databases without considering these sources of uncertainty. Additionally, existing approaches rely solely on statistically validating representative samples through experiments conducted over long periods of time.

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