M-YOLOv8:用于海洋环境的多尺度和不对称感知船舶检测
Peizheng Li1,2, Dayong Qiao1, Jianyi Mu2
1School of Mechanical Engineering, Northwestern Polytechnical University, Xi'an 710072, China.
Sensors (Basel, Switzerland)
|January 28, 2026
概括
这项研究介绍了M2S-YOLOv8,一种增强的船舶检测框架. 它在具有挑战性的海洋条件下提高了准确性,在无人驾驶海洋平台上提供了高效的部署.
科学领域:
- 计算机视觉 计算机视觉
- 海洋机器人 海洋机器人
- 人工智能的人工智能
背景情况:
- 船舶检测对于海洋环境感知至关重要,但面临着目标遮蔽,多尺度目标,不对称几何形状和恶劣的成像条件等挑战.
- 现有的基于视觉的方法与图像质量下降和在真实海洋环境中常见的复杂场景作斗争.
研究的目的:
- 开发一个改进的船舶检测框架,M2S-YOLOv8,解决复杂海洋环境中当前方法的局限性.
- 提高对多尺度和不对称船只目标的感知,提高特征融合效率,稳定小目标回归.
主要方法:
- 基于YOLOv8的拟议的M2S-YOLOv8框架,将多尺度不对称感知并行补丁智能注意 (MSA-PPA) 模块纳入骨干.
- 在部引入了一个可变形卷积上采样 (DCNUpsample) 运算符,以实现高效的自适应特征融合.
- 开发了一种基于瓦斯斯坦距离的加权规范化CIoU (WA-CIoU) 损失函数,以提高小目标回归稳定性.
主要成果:
- M2S-YOLOv8在无人船舟山感知数据集 (UZPD) 和新加坡海事数据集 (SMD) 上表现出更好的性能.
- 该框架实现了轻量级设计和实时推断能力之间的平衡.
- 观察到对多尺度和不对称目标的感知增强,高效的特征融合和稳定的定位.
结论:
- M2S-YOLOv8提供了一个强大的解决方案,用于在具有挑战性的海洋环境中检测船舶.
- 拟议的改进显著提高了检测准确性和定位稳定性.
- 该框架显示了在边缘设备上部署无人驾驶海洋平台的强大潜力,以获得可靠的海洋感知.
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