研究通过卡尔曼波器优化改进具有立体视觉的船舶的距离精度
Zhongbo Peng1, Jie Han1, Liang Tong1
1School of Shipping and Naval Architecture, Chongqing Jiaotong University, Chongqing, China.
PloS one
|November 5, 2024
概括
这项研究引入了一种改进的内陆水道船舶检测和距离系统. 新的算法提高了实时性能和准确性,提高了船舶的航行安全.
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
- 海事技术 在海事技术.
背景情况:
- 确保内陆水路船舶的航行安全和环境感知至关重要.
- 现有的船舶检测模型面临的挑战是参数大,计算复杂度高,实时性能差.
- 环境因素可能会导致测量数据不稳定,以深度估计范围.
研究的目的:
- 提高内陆水路船舶的航行安全和环境感知.
- 开发一个更高效,更准确的船舶检测算法.
- 提高船舶在复杂环境中的强度和精度.
主要方法:
- 提出了MS-YOLOv5s船只目标检测算法,将YOLOv5s骨干替换为MobileNetV3-Small,以提高速度.
- 开发了一种双眼卡尔曼波器融合测距算法,以解决不稳定的测量数据.
- 在参数大小,检测速率,精度和mAP方面评估检测算法的性能.
- 在特定距离内评估范围算法的标准偏差和错误控制.
主要成果:
- MS-YOLOv5s模型实现了参数大小为3.55M (50.49%的YOLOv5s),检测率为50.28 FPS,精度为96.80%,mAP为98.40%.
- 双筒卡尔曼波器融合算法将范围结果的标准偏差降低到6.032μm,比传统方法小一点数.
- 距离错误控制在3%以内,目标在20m以内.
结论:
- 拟议的MS-YOLOv5s算法为船舶检测提供了高精度和低计算需求之间的平衡.
- 双筒卡尔曼波器融合算法显著提高了复杂的内陆水道环境中测量范围的稳定性.
- 综合系统提高了船舶的环境感知能力和航行安全,有助于智能船舶的发展.
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