发表机构
School of Engineering, University of Southampton; Institute of Industrial Science, The University of Tokyo; Ocean Infinity; Marine Autonomous and Robotic Systems, National Oceanography Centre; Voyis Imaging Inc.（南安普顿大学工程学院; 东京大学工业科学研究所; Ocean Infinity公司; 国家海洋学中心海洋自主与机器人系统; Voyis Imaging Inc公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究通过低带宽通信链路实时处理和传输AUV图像的方法，利用AI技术识别代表性或相似图像，结合元数据压缩传输，经实地验证可大幅减少数据量，快速传输数据摘要。
AI 中文摘要
本文介绍了一种通过低带宽通信链路对自主水下航行器（AUV）图像进行实时处理和传输的方法。利用人工智能（AI）技术识别最能代表整个数据集的一组图像，或自动找到与给定查询图像最相似的图像传输给操作员。结合大量图像的元数据，所选图像的压缩版本可通过卫星通信链路或水下调制解调器传输，为岸上操作员提供AUV收集图像类型的信息。来自英国海岸和大加那利岛三次使用不同AUV和成像系统部署的数据在实地验证了该方法。与原始数据大小相比，数据量减少了近40万倍，能在低带宽卫星通信上仅用34多分钟传输长达2小时47分钟测绘任务的数据摘要。
英文摘要
This paper introduces a method for real-time processing and transmission of autonomous underwater vehicle (AUV) imagery over low-bandwidth communication links. It leverages artificial intelligence (AI) techniques to identify a set of images that best represent an entire dataset, or automatically finds the most similar images to a given query image for transmission to operators. Combined with metadata of a larger set of images, compressed versions of the selected images can be transmitted over satellite communication links or underwater modems, and provide operators on shore with information about the type of imagery the AUV is collecting while it is still deployed. Data from three deployments off the coast of the UK and in Gran Canaria using different AUVs and imaging systems demonstrate the method in the field. It achieved an almost 400,000-fold reduction in data volume compared to the raw data size, enabling transmission of data summaries of a 2-hour 47-minute-long mapping mission in just over 34 minutes over low-bandwidth satellite communication.
