Underwater target classification method considering robustness of deep learning model

Abstract

The invention discloses an underwater target classification method considering robustness of a deep learning model, and aims to solve the problem that the existing deep learning model is low in underwater target classification accuracy. The method comprises the following steps: predicting a collected underwater target data training set by using a trained original model to obtain a set of all correctly classified samples and a set of all wrongly classified samples; inputting the set of all the classified error samples into the trained original model, and performing clustering and feature compensation on the features of the classified error samples to obtain feature compensation of the classified error samples; inputting feature compensation into the trained original model to obtain an original model after feature compensation; inputting the underwater target data training set into the original model after feature compensation, and outputting samples with wrong classification; building an adversarial training model to obtain a trained adversarial training model; performing weighted combination on the adversarial training model and the original model after feature compensation to generate a deep learning model; the method belongs to the underwater target classification field.

Description

technical field [0001] The invention relates to a method for classifying underwater targets, in particular to a method for classifying underwater targets considering the robustness of a deep learning model, and belongs to the field of classifying underwater targets. Background technique [0002] It is gradually difficult to apply traditional methods for underwater classification tasks. In recent years, deep learning models have achieved remarkable results in recognition and classification tasks, so deep learning methods have also been used for underwater classification tasks. However, due to the particularity of the underwater environment, the underwater target data used for identification and classification is also greatly disturbed by the environment, and the deep learning model is more sensitive to the input of disturbed underwater target data, and is especially susceptible to the influence of adversarial samples, resulting in The output of the model is not stable enough,...

AI Technical Summary

Problems solved by technology

[0003] In order to solve the problem of low accuracy rate of underwater target classification by the existing deep learning model, the present invention further proposes a method for underwater target classification considering the robustness of the deep learning model

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