Computer vision-based detection of fish from acoustic backscatter time series

我们提出的研究解决了利用计算机视觉技术从回声测深数据中检测视觉模式的新方法。从更传统的声学数据处理模式切换到计算机视觉算法可能会为回声测深数据提供新的见解，并可能使我们的合作伙伴公司丰富他们的自动数据处理服务组合。**我们的工业合作伙伴是ASL环境科学公司（ASL），一家总部位于不列颠哥伦比亚省**维多利亚的员工所有公司。ASL是全球环境监测创新解决方案的领导者，在海洋学、声学、遥感和冰研究产品以及科学咨询服务方面拥有40多年的经验。自2000年以来，ASL一直在建造声学反向散射回声测深仪。迄今为止，ASL在处理其声学后向散射回声测深仪收集的数据方面提供了非常有限的支持。**考虑到ASL已经制造的165个回声测深装置，客户对非声学人员的变化，以及该技术在更多应用中的采用，现在有机会为ASL的产品组合添加新的数据处理服务。有许多潜在的应用领域是ASL感兴趣的探索，例如用于悬浮沉积物分类的自动数据处理，脆弱的冰，海洋湍流，水中的油，浮游动物/虾和鳍鱼。**此次Engage合作将进行短期（6个月）的可行性研究，从计算机视觉的角度解决鳍鱼检测问题。我们将利用回声图中的视觉线索**，用计算机视觉算法来检测和分类鱼类。将探讨传统的分割方法，以及深度学习的最新发展。

Our proposed research addresses novel ways of detecting visual patterns from echosounder data using**computer vision techniques. Switching from the more traditional acoustic data processing paradigm to**computer vision algorithms may provide new insights into echosounder data, and may enable our partner**company to enrich their portfolio of automatic data processing services.**Our industrial partner is ASL Environmental Sciences Inc (ASL), an employee-owned company based in**Victoria, BC. ASL is a global leader in innovative solutions for environmental monitoring and has over 40**years of experience in oceanographic, acoustic, remote sensing and ice research products and scientific**consulting services. Since 2000, ASL has been building acoustic backscatter echosounders. To date, ASL has**provided very limited support for processing the data gathered from its acoustic backscatter echosounders.**Considering the 165 echosounder units that ASL has manufactured, the change in the clientele towards**non-acousticians, and the adoption of the technology for a greater number of applications, there is now an**opportunity to add new data processing services to ASL's portfolio. There are a number of potential application**areas that ASL is interested in exploring, such as automatic data processing for classification of suspended**sediments, frazil ice, ocean turbulence, oil in water, zooplankton/shrimp and fin fish.**This Engage collaboration will enable a focused short-term (six months) feasibility study addressing the**problem of fin fish detection from a computer vision perspective. We will exploit visual cues from echograms**to detect and classify fish with computer vision algorithms. Traditional segmentation methods, as well as recent**developments in deep learning will be explored.