RAPID: Collaborative Research: Machine Learning for Dehazing Unmanned Aerial System Imagery from Volcanic Eruptions

RAPID：协作研究：用于消除火山喷发无人机系统图像雾霾的机器学习

• 批准号: 1840873
• 负责人: Robin Murphy
• 金额: $ 8.07万
• 依托单位: Texas A&M Engineering Experiment Station
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-08-01 至 2019-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1840873&HistoricalAwards=false
• 关键词: RAPID; Collaborative; Research; Machine; Learning

The ongoing eruption of the Kilauea volcano in Hawaii is the first reported time that small unmanned aerial systems (UAS) have been used for the emergency response to a volcanic eruption. The Center for Robot-Assisted Search and Rescue (CRASAR) flew 44 small UAS flights for the Hilo Fire Department and Hawaii County Civil Defense. The eruption imagery was partially occluded by plumes of steam carrying toxic gases, something that had not been encountered before. The plumes interfere with responders comprehending the tactical situation because it obscures the ground below and often prevents software from generating useful surface maps. While volcanic eruptions are fairly rare, the same plume problem is likely to occur in other hazardous material events. Machine learning techniques for dehazing were only partially successful because plumes present a very different set of challenges than removing urban haze or smog. This project conducts rapid research to remove or reduce plumes, from stills and video, in near real-time in order to support responses to the ongoing disaster. It will make the datasets available so that they can be used for training and evaluating new machine learning algorithms. The project will host a follow up workshop at the 2019 AAAI Conference on Artificial Intelligence in Hawaii.This project creates a UAS open-source imagery dataset from the ongoing Leilani, Hawaii, volcanic eruption event. It uses the dataset to expand and refine dehazing algorithms that will help Hawaii public safety agencies and volcanologists see through the plumes of steam and gas that is interfering with mapping the extent and volume of the lava. Plumes of steam mingled with sulfur dioxide interfered with interpreting the boundaries of the lava field and introduced errors into stitching images together or caused details to be averaged out. Smog is a homogeneous, thin visual phenomenon while plumes are heterogeneous and thick, limiting the utility of current techniques and requiring focused research. The dataset offers an opportunity for a corpus of real imagery that can serve as machine learning training data and enable comparison of before and after results. The intellectual merit of the project is twofold. It provides a unique opportunity to explore a new area of machine learning for heterogeneous, thick plumes in images. The comprehensive dataset will enable foundational work in computer vision, machine learning, and emergency informatics. The research will immediately improve emergency management of the Leilani eruption event and emergency management in general.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

夏威夷的基拉韦阿火山的持续爆发是第一次报道的时间是，已将小型无人空中系统（UAS）用于对火山喷发的紧急响应。机器人协助搜救中心（Crasar）为希洛消防局和夏威夷县民防飞行了44次小型UAS航班。喷发图像被携带有毒气体的蒸汽羽毛所遮住，这是以前从未遇到过的。羽毛干扰了理解战术情况的响应者，因为它遮盖了下面的地面，并且通常会阻止软件生成有用的表面图。尽管火山喷发相当罕见，但在其他有害物质事件中可能会出现同样的羽流问题。除掩饰的机器学习技术仅部分成功，因为羽毛与消除城市雾霾或烟雾相比，羽毛构成了截然不同的挑战。该项目进行了快速的研究，以近乎实时的实时删除或减少视频，以支持对正在进行的灾难的反应。它将使数据集可用，以便它们可以用于培训和评估新机器学习算法。该项目将在夏威夷的2019年AAAI人工智能会议上举办一个后续研讨会。该项目由正在进行的夏威夷夏威夷莱拉尼，火山喷发活动创建UAS开源图像数据集。它利用数据集扩展和完善飞行算法，这些算法将帮助夏威夷公共安全机构和火山学家看到蒸汽和天然气的羽毛，这些蒸汽和气体正在干扰熔岩的程度和数量。与二氧化硫混合的蒸汽干扰了解释熔岩场的边界，并将误差引入将图像拼接在一起或导致细节平均。烟雾是一种均匀的，薄的视觉现象，而羽流是异质和厚的，限制了当前技术的效用，需要集中研究。该数据集为真实图像的语料库提供了机会，可以用作机器学习培训数据，并可以比较前后的结果。该项目的智力优点是双重的。它为探索机器学习的新领域提供了一个独特的机会，以实现图像中异质，厚的羽毛。综合数据集将在计算机视觉，机器学习和紧急信息学方面实现基础工作。 这项研究将立即改善莱拉尼喷发事件和一般紧急管理的紧急管理。该奖项反映了NSF的法定任务，并使用基金会的知识分子优点和更广泛的影响审查标准，被认为值得通过评估来获得支持。

项目成果

Use of Small Unmanned Aerial Systems for Tactical Response during Kilauea Volcano Lower East Rift Zone event

在基拉韦厄火山东裂谷区事件期间使用小型无人机系统进行战术响应

• DOI: 10.1109/ssrr.2018.8468626
• 发表时间: 2018
• 期刊: Use of Small Unmanned Aerial Systems for Tactical Response during Kilauea Volcano Lower East Rift Zone event
• 作者: Adams, Justin;Hart, Laura;McBride, Jon;Merrick, David;Murphy, Robin
• 通讯作者: Murphy, Robin

Humans and Robots in Off-Normal Applications and Emergencie

非正常应用和紧急情况下的人类和机器人

• 发表时间: 2019
• 期刊: Advances in Human Factors in Robots and Unmanned Systems. AHFE 2019
• 作者: Murphy, Robin R.