基于深度学习的舰船目标重识别技术

Vessel reidentification technology based on deep learning

面向行人和车辆的重识别技术已在情报分析领域得到成功应用,但是对于舰船目标的重识别技术研究还比较缺乏,对此本文提出了一种基于双重特征融合的海上去雾重识别网络,用于海面舰船目标的情报分析和监管。首先,为了降低负样本对特征的影响,采用了视角辅助的自适应查询扩展方法和基于相似度的特征融合方法。其次,在重识别分支的浅层嵌入了去雾分支,利用权重共享技术提取无雾特征,并通过上采样技术和金字塔模型重建去雾图像,以增强网络在低能见度场景下的识别能力。最后,提出了一种基于伪交并比的非极大值抑制方法,通过修正检测框置信度来提高船舶目标的检测精度。实验结果表明,所提方法的性能优于现有方法,并且各模块对网络性能都有贡献。

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.