AI筛选水下图像，大幅压缩数据量实现实时远程感知
Remote Awareness of Seafloor Images Collected by AUVs over Low-Bandwidth Communication Links
- 用AI自动挑选最具代表性的水下图像，减少冗余
- 数据量减少近40万倍，2小时47分钟任务仅需34分钟传输
- 适合深海探测、低带宽通信场景下的实时决策支持
本文提出一种在低带宽通信链路下对自主水下航行器（AUV）影像进行实时处理与传输的方法。利用人工智能技术识别最能代表整个数据集的图像，或自动匹配与查询图像最相似的图像进行传输。结合大范围影像的元数据，将选定图像的压缩版本通过卫星通信链路或水下调制解调器发送，使岸上操作员在任务执行期间即可获取AUV采集影像的类型信息。在英国海岸及大加那利岛三次不同AUV与成像系统部署中验证了该方法的有效性。相比原始数据量，实现了近40万倍的数据量压缩，可在低带宽卫星通信下，仅用34分钟以上完成一场持续2小时47分钟测绘任务的数据摘要传输。
原文摘要 · Abstract (English)
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.
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