潜水道的浅泥检测算法基于改进的YOLOv5s.
1School of Marine Technology and Geomatics, Jiangsu Ocean University, Lianyungang, 222005, China.
Heliyon
|May 23, 2024
概括
这项研究引入了一种增强的YOLOv5s算法,用于检测海底泥,改善海运航道的航行安全. 新方法在识别水下危险方面实现了高精度和高效率.
科学领域:
- 海洋地质工程 海洋地质工程
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- 潜水泥对海上航道航行安全构成重大风险.
- 传统的海底泥检测方法往往是低效的,缺乏准确性.
- 准确识别浅水潜水泥对于安全的疏和运河维护至关重要.
研究的目的:
- 通过使用改进的YOLOv5s模型,提出一种增强的浅水潜艇泥检测算法.
- 提高在海上道中识别海底泥危险的准确性和有效性.
- 提供一种新的技术方法,以确保海底道的疏安全运行和维护.
主要方法:
- 利用来自连云港的下底形仪数据,获取浅泥音印图像.
- 将C2F特征模块集成到骨干中,以增强梯度流和特征提取.
- 在部模块中集成有效的多尺度注意力 (EMA) 机制,以优化通道尺寸和效率.
- 引入了规范化瓦瑟斯坦距离 (NWD) 损失函数,以改善边界框回归和多尺度缺陷处理.
主要成果:
- 与原始YOLOv5s和其他检测算法相比,改进的YOLOv5s-EF算法表现出卓越的性能.
- 实现了97.8%的验证设置精度率,97.6%的回忆率和97.7%的F1值.
- 记录了98.2%的平均平均精度 (mAP) @0.5和51.8的FPS,表明检测精度和速度很高.
结论:
- 增强的YOLOv5s-EF算法为浅水潜艇泥检测提供了强大而准确的解决方案.
- 集成特定的模块和损失功能的显著提高了检测性能.
- 这种新的方法对于提高海上运河航行和疏操作的安全性和效率至关重要.
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