在复杂的海上环境中实现强大的船舶目标识别的等级局部-全球特征融合网络
Xuanhe Liu1, Shuning Zhang1, Si Chen1
1School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China.
Sensors (Basel, Switzerland)
|January 10, 2026
概括
本研究引入了一种新的层次局部-全球特征融合网络 (HLGF-Net),用于改进船舶目标识别. 高高频网络有效地融合了本地和全球特征,提高了复杂的海上环境中的准确性.
科学领域:
- 计算机视觉 计算机视觉
- 海事监督部门的监督工作
- 深度学习 (Deep Learning) 是一种深度学习.
背景情况:
- 准确的船舶目标识别对于海上安全和保障至关重要.
- 挑战包括背景杂乱,不同的目标尺度,以及与单一网络模型相比,有限的特征可区分性.
研究的目的:
- 提出一个新的等级局部-全球特征融合网络 (HLGF-Net),用于强大的船舶目标识别.
- 通过整合本地结构线索和全球语义依赖来增强特征提取.
主要方法:
- 开发了HLGF-Net,结合了CNN编码器用于本地特征和全球语义依赖的变压器.
- 利用堆叠的变压器块来逐步建立层次依赖关系以实现全面的功能集成.
- 在船舶识别数据集上进行了广泛的实验,以验证模型的有效性.
主要成果:
- 在传统的CNN,纯变形金刚,以及其他近期视觉架构上,HLGF-Net表现出了卓越的性能.
- 该模型在杂乱的背景,部分封闭和有限的目标样本场景中显示出显著的改进.
- 在捕捉细粒度的局部轮和远程的全球上下文关系方面实现了增强的能力.
结论:
- HLGF-Net提供了一个有效的解决方案,用于强大的海上目标识别.
- 拟议的框架为深度学习模型中集成层次的本地-全球特征提供了总体策略.
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