The error sample feature compensation method for improving the robustness of underwater classification and recognition models

With the intensification of ocean exploration and development in recent years, the navigation equipment in the marine environment has become increasingly diversified, making the marine environment more complex. Traditional methods for underwater target recognition are gradually becoming less applicable and unable to achieve better results. With the application of deep learning in underwater target recognition, the robustness of deep learning models used for underwater target recognition is crucial due to the significant environmental interference in underwater target data and the susceptibility of deep learning models to adversarial samples. This paper proposes an error sample feature compensation method for improving the robustness of deep learning models for underwater target recognition, focusing on the problem of the significant impact of sample data quality on the robustness of deep learning models for underwater target recognition. The method innovatively divides error samples into difficult-to-improve samples and easy-to-improve samples and proposes an adversarial training method combined with classification conditions. At the same time, the method uses a weighted index of model accuracy to combine adversarial training models with feature compensation methods, further improving the robustness of deep learning models for underwater target recognition tasks. Finally, the method is validated on an underwater dataset, and the results show that the proposed method improves the robustness of deep learning models used for underwater target recognition.