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Re-identification technology for pedestrians and vehicles has been successfully applied in the field of intelligence analysis. However, there is a lack of research on re-identification technology for ship targets. In this paper, we propose a double-feature fusion-based maritime defogging re-identification network for intelligence analysis and supervision of ship targets. To reduce the impact of negative samples on features, we adopt a perspective-assisted adaptive query expansion method and a similarity-based feature fusion method. Furthermore, a defogging branch is embedded in the shallow layer of the re-identification branch. This branch utilizes weight sharing technology to extract fog-free features. The defogged image is then reconstructed using upsampling technology and the pyramid model, enhancing the recognition ability of the re-identification network in low-visibility scenarios. Finally, a pseudo-IOU based non-maximum suppression method is proposed to enhance the detection accuracy of ship targets. This method modifies the confidence of the detection frame. Experimental results demonstrate that the proposed method outperforms existing methods, and each module contributes to the network’s performance.

Semi-supervised learning has been proven to be effective in utilizing unlabeled samples to mitigate the problem of limited labeled data. Traditional semi-supervised learning methods generate pseudo-labels for unlabeled samples and train the classifier using both labeled and pseudo-labeled samples. However, in data-scarce scenarios, reliance on labeled samples for initial classifier generation can degrade performance. Methods based on consistency regularization have shown promising results by encouraging consistent outputs for different semantic variations of the same sample obtained through diverse augmentation techniques. However, existing methods typically utilize only weak and strong augmentation variants, limiting information extraction. Therefore, a multi-augmentation contrastive semi-supervised learning method (MAC-SSL) is proposed. MAC-SSL introduces moderate augmentation, combining outputs from moderately and weakly augmented unlabeled images to generate pseudo-labels. Cross-entropy loss ensures consistency between strongly augmented image outputs and pseudo-labels. Furthermore, the MixUP is adopted to blend outputs from labeled and unlabeled images, enhancing consistency between re-augmented outputs and new pseudo-labels. The proposed method achieves a state-of-the-art performance (accuracy) through extensive experiments conducted on multiple datasets with varying numbers of labeled samples. Ablation studies further investigate each component's significance. [ABSTRACT FROM AUTHOR]

To address the problem that the performance of the detector in airborne magnetic anomaly detection (MAD) is terrible, a stochastic resonance (SR) detection algorithm based on orthonormalized basis function (OBF-SR) is proposed for MAD under low signal-to-noise ratio conditions. The signal contaminated by noise is first preprocessed by the OBF method, where the sum of the three components in the OBF space is selected as the SR system input. Then, a parallel SR system with different initial states is designed to detect the signal. Finally, the simulation analysis of MAD methods is performed to draw a comparison between the OBF-SR method, the typical SR method, and the OBF method. The results show that the OBF-SR method outperforms the SR and OBF methods in the detection probability and detection range under the same conditions. [ABSTRACT FROM AUTHOR]