用于侧扫声纳图像沉船分割的机器学习：数据集与基准
Machine Learning for Shipwreck Segmentation from Side Scan Sonar Imagery: Dataset and Benchmark
AI总结：
针对水下标注基准数据集匮乏问题，提出AI4Shipwrecks开源数据集，包含286张高分辨率标注侧扫声纳图像，并基于AUV调查与专家标注进行有监督分割基准实验，推动海洋探索机器学习创新。
摘要：
开源基准数据集一直是推动陆地应用中机器人感知机器学习发展的关键组成部分。基准数据集促进了最先进机器学习方法的广泛开发，这些方法需要大型数据集来进行训练、验证以及与竞争方法的全面比较。水下环境带来了多项操作挑战，阻碍了为海洋机器人感知收集大型基准数据集的努力。此外，相对于搜索空间的大小，感兴趣目标的低丰度导致收集特定任务有用数据集所需的时间和成本增加。因此，水下应用的标注基准数据集可用性有限。我们提出了AI4Shipwrecks数据集，包含28个不同的沉船，共计286张高分辨率标注侧扫声纳图像，以推进自主声纳图像理解的最先进技术。我们利用密歇根州休伦湖Thunder Bay National Marine Sanctuary中目标的独特丰度，通过自主水下航行器（AUV）的调查收集并编制了声纳图像基准数据集。我们咨询了专业海洋考古学家对机器人收集的数据进行标注。然后，我们利用该数据集进行基准实验，以比较最先进的有监督分割方法，并提出了该领域机遇与开放挑战的见解。数据集和基准测试工具将作为开源基准数据集发布，以激发五大湖和海洋探索机器学习的创新。数据集和配套软件可在https://umfieldrobotics.github.io/ai4shipwrecks/获取。
英文摘要：
Open-source benchmark datasets have been a critical component for advancing machine learning for robot perception in terrestrial applications. Benchmark datasets enable the widespread development of state-of-the-art machine learning methods, which require large datasets for training, validation, and thorough comparison to competing approaches. Underwater environments impose several operational challenges that hinder efforts to collect large benchmark datasets for marine robot perception. Furthermore, a low abundance of targets of interest relative to the size of the search space leads to increased time and cost required to collect useful datasets for a specific task. As a result, there is limited availability of labeled benchmark datasets for underwater applications. We present the AI4Shipwrecks dataset, which consists of 28 distinct shipwrecks totaling 286 high-resolution labeled side scan sonar images to advance the state-of-the-art in autonomous sonar image understanding. We leverage the unique abundance of targets in Thunder Bay National Marine Sanctuary in Lake Huron, MI, to collect and compile a sonar imagery benchmark dataset through surveys with an autonomous underwater vehicle (AUV). We consulted with expert marine archaeologists for the labeling of robotically gathered data. We then leverage this dataset to perform benchmark experiments for comparison of state-of-the-art supervised segmentation methods, and we present insights on opportunities and open challenges for the field. The dataset and benchmarking tools will be released as an open-source benchmark dataset to spur innovation in machine learning for Great Lakes and ocean exploration. The dataset and accompanying software are available at https://umfieldrobotics.github.io/ai4shipwrecks/.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
