Vessel reidentification technology based on deep learning
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 tar-gets.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-i-dentification 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 dem-onstrate that the proposed method outperforms existing methods,and each module contributes to the network's performance.
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