Computer Vision-Based Deep Learning Algorithms for Detecting Marine Life and Physical Phenomena from Acoustic Backscatter Time Series

基于计算机视觉的深度学习算法，用于从声学反向散射时间序列中检测海洋生物和物理现象

• 批准号: 576751-2022
• 负责人: BranzanAlbu, AlexandraA
• 金额: $ 2.19万
• 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: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=751776
• 关键词: Computer; Vision; Based; Deep; Learning

Large quantities of data are constantly acquired during underwater acoustic surveys for environmental monitoring and resources management. The data, visualized as 2D images, are typically analyzed manually or semi-automatically by experts (marine biologists, acousticians, oceanographers), which is time-consuming and prone to errors and inter-expert disagreements. The goal of the proposed research is to develop new software tools for the automated processing and analysis of underwater acoustic data acquired with echosounders, using computer vision-based deep learning methods.We anticipate that this research, carried out in partnership with ASL Environmental Sciences Inc., a British Columbian company, will allow for the automatic detection of marine life, such as eulachon, sandlance, arctic cod, jellyfish, zooplankton, as well as various phenomena near the sea surface and sea bottom, such as air bubbles, waves, ice keels, and suspended sediments, from underwater acoustic data. The potential impacts are significant with respect to efforts in species abundance tracking and environmental monitoring, allowing for a switch from the traditional data analyses towards novel automatic methods reducing processing times, required man-power, and inconsistencies in the results. In addition, this research focuses on regions (depths) that are typically discarded as too difficult to analyze manually. An impact of the automated processing associated with this research is that vast amounts of potentially valuable data may be analyzed rather than discarded, offering a more comprehensive picture of the oceans.The topic is of high importance to Canada as a global leader in sustainable fisheries and ocean resource management, and in research around underwater climate change impacts. The developed software will benefit Canada by providing Canadian researchers with improved and economical means for acoustic data analysis.

在进行环境监测和资源管理的水下声学勘测期间，不断获得大量数据。可视化为2D图像的数据通常由专家（海洋生物学家，声学家，海洋学家）手动或半自动分析，这是耗时的，并且容易出现错误和专家间的分歧。拟议研究的目标是开发新的软件工具，用于使用基于计算机视觉的深度学习方法自动处理和分析回声测深仪采集的水声数据。我们预计，这项与ASL环境科学公司合作开展的研究，不列颠哥伦比亚省的一家公司，将允许从水下声学数据中自动探测海洋生物，如eulachon、sandlance、北极鳕鱼、水母、浮游动物，以及海面和海底附近的各种现象，如气泡、波浪、冰龙骨和悬浮沉积物。潜在的影响是显着的物种丰度跟踪和环境监测方面的努力，允许从传统的数据分析转向新的自动方法，减少处理时间，所需的人力，并在结果的不一致性。此外，本研究重点关注通常因手动分析太困难而被丢弃的区域（深度）。与这项研究相关的自动化处理的一个影响是，可以分析而不是丢弃大量潜在有价值的数据，从而提供更全面的海洋情况。作为可持续渔业和海洋资源管理的全球领导者，加拿大在水下气候变化影响的研究方面非常重要。开发的软件将为加拿大研究人员提供改进且经济的声学数据分析方法，使加拿大受益。