使用改进的双域YOLOv11-UOS进行准确的实时水下物体细分,并使用物理引导的自适应增强和注意力提升
N Deluxni1, Pradeep Sudhakaran2, Roobaea Alroobaea3
1Department of Computing Technologies, School of Computing, SRM Institute of Science and Technology, Kattankulathur, Chengalpattu, Tamilnadu, 603203, India.
Scientific reports
|January 7, 2026
概括
这项研究引入了先进的YOLOv11-UOS系统,用于精确的水下物体检测. 该方法提高了图像质量,并使用注意力机制来改善在具有挑战性的海洋环境中的实例细分.
科学领域:
- 计算机视觉 计算机视觉
- 机器人技术 机器人技术 机器人技术
- 海洋技术 海洋技术
背景情况:
- 水下物体检测对于海洋勘探,国防和自主导航至关重要.
- 挑战包括可见性差,色彩扭曲和复杂的背景,妨碍准确的识别.
研究的目的:
- 开发一个先进的实例细分系统,用于准确的水下物体检测.
- 提高在具有挑战性的水下条件下检测性能.
主要方法:
- 整合注意力增强技术和物理引导的自适应图像增强.
- 使用双域YOLOv11-UOS架构与双重注意力提升模块.
- 解决波长依赖的衰减和散射,以获得颜色保真性和对比度恢复.
主要成果:
- 拟议的方法在定制水下数据集上的细分精度上明显优于基线方法.
- 证明了对部分遮,度和照明变化的坚固性.
- 实现了适合实时应用的高精度和效率.
结论:
- 开发的YOLOv11-UOS系统为水下物体检测提供了精确有效的解决方案.
- 该系统能够处理图像退化和屏蔽,这使得它非常适合实时水下机器人.
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