自主水下车辆:识别图像采集的关键问题和未来前景
Alberto Monterroso Muñoz1, Maria-Jose Moron-Fernández2, Daniel Cascado-Caballero2
1Department of Applied Mathematics I, Universidad de Sevilla, 41012 Sevilla, Spain.
Sensors (Basel, Switzerland)
|July 11, 2023
概括
本研究确定了自动水下车辆 (AUV) 水下成像的关键问题. 它提出了一个全球工作流程,以解决从图像获取到算法处理的挑战,以提高AUV性能.
科学领域:
- 机器人和计算机视觉 机器人和计算机视觉
- 海洋技术 海洋技术
- 图像处理 图像处理
背景情况:
- 水下成像对于AUV导航和视觉系统至关重要.
- 无人驾驶汽车的快速发展凸显了在水下图像处理中需要标准化的方法的需要.
- 现有的研究缺乏对AUV成像挑战的全面,可概括的解决方案.
研究的目的:
- 在自主水下车辆 (AUV) 成像的整个过程中识别关键问题.
- 分析图像采集,增强,质量评估和算法处理挑战.
- 提出AUV成像的全球工作流程,概述未来的要求和前景.
主要方法:
- 分析了近几十年来120篇AUV文章的统计数据,重点关注最先进的论文.
- 对专业摄影和科学成像在AUV应用中的协同效应的研究.
- 对图像获取,增强,质量评估和马赛克技术的审查.
主要成果:
- 确定了在AUV成像研究中缺乏标准化,通用方法的建议.
- 突出了涵盖光学传感到AUV中的算法处理的关键问题.
- 分析了最近AUV文献中的趋势和差距.
结论:
- 为AUV成像提出了一个全面的全球水下工作流程.
- 提取了未来的需求和新的视角,以推进AUV视觉和导航.
- 解决已识别的问题可以显著提高AUV运营能力.
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