Automatic Visual Analysis of Acoustic Backscatter Time Series for Monitoring Underwater Environments

用于监测水下环境的声学反向散射时间序列的自动可视化分析

• 批准号: 555567-2020
• 负责人: BranzanAlbu, Alexandra
• 金额: $ 1.46万
• 依托单位: University of Victoria
• 依托单位国家: 加拿大
• 项目类别: Alliance Grants
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=705822
• 关键词: Automatic; Visual; Analysis; Acoustic; Backscatter

The goal of this proposal is to develop new software tools for the automated processing and analysis of underwater acoustic data acquired with echosounders. These new tools will be based upon computer vision and machine learning methods. The potential outcomes include the automatic detection of marine life, such as herring, salmon, and eulachon, as well as of various underwater events and physical phenomena, such as ocean turbulence, presence of suspended sediments, and presence of oil in water. The potential impacts are significant with respect to improving the efficiency and accuracy of species abundance tracking and environmental monitoring. They will enable a paradigm shift from the traditional time-consuming, error-prone manual or semi-automatic acoustic survey data analysis towards novel automatic analysis methods, and thus will reduce processing times, required man-power, and inconsistencies in results.

这项建议的目标是开发新的软件工具，用于自动处理和分析回声测深仪获得的水下声学数据。这些新工具将基于计算机视觉和机器学习方法。 潜在的成果包括自动检测鲱鱼、鲑鱼和尤拉琼等海洋生物，以及各种水下事件和物理现象，如海洋湍流、悬浮沉积物的存在和水中石油的存在。 在提高物种丰度跟踪和环境监测的效率和准确性方面，潜在的影响很大。它们将实现从传统的耗时、易出错的手动或半自动声学勘测数据分析向新型自动分析方法的范式转变，从而减少处理时间、所需人力和结果的不一致性。