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

在水声调查中，不断获取大量的数据，用于环境监测和资源管理。数据以2D图像的形式可视化，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，这既耗时又容易出错，专家之间也存在分歧。该研究的目标是利用基于计算机视觉的深度学习方法，开发新的软件工具，用于自动处理和分析由回声测深仪获取的水声数据。我们预计，这项与不列颠哥伦比亚省ASL环境科学公司合作开展的研究，将允许从水声数据中自动检测海洋生物，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。在物种丰度跟踪和环境监测方面，潜在的影响是显著的，允许从传统的数据分析转向新的自动化方法，减少处理时间，所需的人力和结果的不一致性。此外，本研究侧重于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，大量潜在有价值的数据可能会被分析，而不是被丢弃，从而提供更全面的海洋图景。作为可持续渔业和海洋资源管理以及水下气候变化影响研究的全球领导者，该主题对加拿大具有高度重要性。开发的软件将使加拿大受益，为加拿大研究人员提供改进和经济的声学数据分析手段。

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.