海上小型目标图像检测算法基于改进的YOLOv11nn
Zhaohua Liu1,2, Yanli Sun3, Pengfei He1,2
1School of Physics and Electronic, Yantai University, Yantai 264005, China.
Sensors (Basel, Switzerland)
|January 10, 2026
概括
本研究引入了一种改进的YOLOv11n算法,用于在红外和可见光图像中检测小型海上目标,显著减少错过的检测,并增强特征提取,以便更好地监测海洋.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 海事监督部门的监督工作
背景情况:
- 在复杂的公海环境中,小型船只由于其尺寸和有限的功能而存在检测挑战.
- 高错误检测率阻碍了有效的海上监测和安全.
研究的目的:
- 开发一个改进的YOLOv11n算法,用于更好地检测小型海上目标.
- 解决功能信息的局限性,减少红外和可见光成像中错过的检测.
主要方法:
- 引入了BIE模块,用于红外和可见光目标特征的渐进融合.
- 集成的RepViTBlock与C3k2模块 (C3k2-RepViTBlock) 改进了微小目标特征的捕获.
- 嵌入式ConvAttn模块用于小目标的自适应轮特征提取.
主要成果:
- 改进的算法显示,IVship和SeaShips数据集的mAP@0.5分别增加了1.9%和1.7%.
- 在各自的数据集上,平均精度得到了2.2%和2.4%的改进.
- 展示了增强的小型目标检测能力,参数增加最小.
结论:
- 增强的YOLOv11n算法有效地提高了海上小型目标检测的准确性.
- 集成BIE,C3k2-RepViTBlock和ConvAttn模块为复杂的海上环境提供了一个强大的解决方案.
- 该算法为海上监视和安全应用提供了一个轻量级但强大的工具.
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