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.

在用于环境监测和资源管理的水声调查过程中不断获取大量数据。可视化为二维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，这非常耗时且容易出现错误和专家间分歧。拟议研究的目标是开发新的软件工具，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获取的水声数据。我们预计这项研究将与 ASL Environmental Sciences Inc 合作进行.，一家不列颠哥伦比亚省的公司，将允许自动检测海洋生物，如真鱼、沙枪、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、来自水下声学数据的波浪、冰龙骨和悬浮沉积物。对于物种丰度跟踪和环境监测方面的潜在影响是重大的，允许从传统的数据分析转向新颖的自动方法，减少处理时间、所需的人力和结果的不一致。此外，这项研究重点关注通常因难以手动分析而被丢弃的区域（深度）。与这项研究相关的自动化处理的一个影响是，大量具有潜在价值的数据可以被分析而不是被丢弃，从而提供更全面的海洋图景。作为可持续渔业和全球领导者，该主题对于加拿大来说非常重要。海洋资源管理，以及围绕水下气候变化影响的研究。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手段，从而使加拿大受益。