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.

该提案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取的水声数据。这些新工具将基于计算机视觉和机器学习方法。 潜在的结果包括自动检测海洋生物，如鲱鱼、鲑鱼和真鱼，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是否存在石油。 对于提高物种丰度跟踪和环境监测的效率和准确性而言，潜在影响是重大的。它们将使传统耗时、容易出错的手动或半自动声学调查数据分析向新颖的自动分析方法转变，从而减少处理时间、所需的人力和结果的不一致。