MCEM:多线索融合与杂乱不变学习,用于实时SAR船舶检测
Haowei Chen1, Manman He2, Zhen Yang1
1The School of Information Engineering, Jiangxi Science and Technology Normal University, Road of Xuefu, Nanchang 330013, China.
Sensors (Basel, Switzerland)
|September 27, 2025
概括
在合成孔径雷达 (SAR) 图像中检测小型船舶对于海上监视至关重要. 新的多键高效海事探测器 (MCEM) 框架在复杂的海上混乱条件下显著提高了探测精度.
科学领域:
- 海上监督是什么意思?海上监督是什么意思?
- 遥感是一种远程传感.
- 在国防方面使用人工智能.
背景情况:
- 在合成孔径雷达 (SAR) 图像中检测小型船舶对于海上安全至关重要.
- 现有的方法与弱目标签名,海上杂乱和计算需求作斗争,限制了性能.
- 高分辨率表示学习受到噪声抑制和特征保存之间的权衡的阻碍.
研究的目的:
- 在SAR图像中开发用于小型船舶的先进检测框架.
- 克服当前海洋杂乱统计模型和深度学习探测器的局限性.
- 加强在具有挑战性的海洋环境中的海上监视能力.
主要方法:
- 提出了多个cue高效的海事探测器 (MCEM),一个无框架.
- 集成了一个特征提取模块 (FEM),具有适应尺寸的卷积.
- 包含一个功能融合模块 (F2M) 来实现目标-背景脱,以及一个检测头模块 (DHM) 来实现精度-效率平衡.
主要成果:
- 在具有挑战性的SAR数据集 (HRSID和SSDD) 上,MCEM实现了最先进的性能.
- 与YOLOv8等基线模型相比,表现出卓越的性能,平均精度 (APS) 和平均回忆 (AR) 显著改善.
- 在HRSID上达到45.1%的APS,在SSDD上达到77.7%的APL,在高度混乱的条件下显示出高精度.
结论:
- 该MCEM框架提供了强大的海上监控,特别是在公海杂乱中检测小目标.
- FEM,F2M和DHM的协同集成有效地解决了以前方法的局限性.
- MCEM为关键的海上安全应用提供了计算效率高,准确的解决方案.
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