使用式训练和SAHI增强的YOLOv11-OBB的高精度海洋雷达物体检测
1Computer Engineering Department, Giresun University, Giresun 28200, Turkey.
Sensors (Basel, Switzerland)
|February 13, 2026
概括
这项研究使用YOLOv11-OBB模型与切片辅助超推理 (SAHI) 增强了海洋雷达对象检测. 该方法提高了对小目标和杂乱的准确性,这对海上安全和自主导航至关重要.
科学领域:
- 计算机视觉 计算机视觉
- 海洋技术 海洋技术
- 人工智能的人工智能
背景情况:
- 海洋雷达对象检测对于海上情势意识至关重要,但面临海上杂乱和小目标等挑战.
- 现有的方法难以应对尺度变化和对平面位置指示器 (PPI) 雷达图像的干扰.
研究的目的:
- 开发一个高精度的物体检测管道用于海洋雷达图像.
- 为了应对检测小目标和处理雷达数据中规模变化的挑战.
主要方法:
- 整合了式训练,切片辅助超推理 (SAHI) 和YOLOv11.11的面向边界框 (OBB) 变体.
- 开发一个基于轮的半自动注释管道,从雷达数据生成多格式标签.
- 在德国航空航天中心 (DLR) DAAN数据集上进行的实验.
主要成果:
- 带SAHI的瓦式YOLOv11n-OBB模型实现了0.95.5以上的平均平均精度 (mAP@0.5).
- 平均中心定位错误低于10像素,显示出高精度.
- 该方法的性能优于标准基线和其他YOLOv11变体,特别是在小目标上.
- 在边缘硬件上实现近实时推断 (4-6 FPS).
结论:
- 定向界限框 (OBB) 和规模感知策略在复杂的海洋雷达环境中显著提高了检测精度.
- 拟议的管道为海上跟踪和自主导航系统提供了实际优势.
- 轻量级模型可以在边缘设备上实现高效,高性能的对象检测,用于实时应用.
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