Automated processing and error detection in multibeam sonar data

多波束声纳数据的自动处理和错误检测

• 批准号: RGPIN-2020-04296
• 负责人: Church, Ian
• 金额: $ 1.89万
• 依托单位: University of New Brunswick
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 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=716958
• 关键词: Automated; processing; error; detection; multibeam

Multibeam sonar data is inherently noisy and prone to errors due to the complexity of the marine environment. Recently, multibeam sonar systems have been implemented on autonomous, or minimally supervised, vessels which either follow preset instructions to map the seafloor or acquire data as the platform transits from one location to another. Autonomous systems provide several benefits to traditional platforms, namely improved safety, as they can map uncharted areas and remove personnel from the vessel. However, the data from these systems do not have the benefits of constant supervision by a sonar operator or data processor; therefore, problems with the data are not immediately identified and corrected and must be compensated for in post-processing. The delay then propagates to an imbalance in the data acquisition to processing time and has slowed the adoption of these data collection platforms. This proposed research program will improve the efficiency of multibeam data processing for autonomous vessels through near-real-time identification of system blunders, environmental artifacts and noise. The result will be a notification and classification system to alert operators of data or system problems, flag noise in the depth measurements, and reduce user interaction with the data. Five objectives make up the research program. 1. Identify and monitor real-time data outputs during acquisition to identify system errors; 2. Automate post-processing of environmental variables to minimize environmental uncertainty; 3. Perform an uncertainty assessment of resulting sonar data; 4. Establish methods for multibeam sonar noise identification and error classification using three-dimensional deep learning; 5. Analyze noise sources and detected errors to improve future autonomous systems; Multibeam sonar datasets for training and testing will be acquired from two vessels: the University of New Brunswick survey launch Heron, working in coastal British Columbia, and the Canadian Coast Guard Ship Amundsen, working throughout the Canadian Arctic. The combination of computational programming, experimental design, and field experience form an immersive HQP training environment for HQP of diverse backgrounds. Program HQP will be introduced to the latest in three-dimensional deep learning algorithms, multibeam sonar data acquisition and processing protocols, and statistical testing in an inclusive training environment within an ocean mapping focused engineering research laboratory. Improving the application of autonomous survey platforms and removing personnel from traditional survey vessels will improve the efficiency of seafloor mapping, limit marine accidents, and reduce the costs of seafloor mapping, especially in remote areas. Only a small percentage of Canadian and international waters are mapped to modern standards, and improving the ability for autonomous vessels to collect this crucial data will provide benefits to Canadians and others around the world.

由于海洋环境的复杂性，多层声纳数据本质上是嘈杂的，容易出现错误。最近，多层声纳系统已在自主或最小监督的船只上实施，该船只遵循预设指令以映射海底或获取数据作为平台从一个位置转移到另一个位置。自主系统为传统平台提供了一些好处，即提高安全性，因为它们可以绘制未知区域并将人员从船只中移走。但是，来自这些系统的数据没有声纳操作员或数据处理器不断监督的好处。因此，数据问题未立即确定和纠正，必须在后处理中得到补偿。然后，延迟传播了数据获取时间的不平衡处理时间，并减慢了这些数据收集平台的采用。 该提出的研究计划将通过近实时的系统漏洞，环境工件和噪声来提高自动船只数据处理的效率。结果将是通知和分类系统，以提醒操作员数据或系统问题，在深度测量中标记噪声，并减少用户与数据的互动。五个目标构成了研究计划。 1。在获取过程中识别和监视实时数据输出以识别系统错误； 2。自动化环境变量的后处理，以最大程度地减少环境不确定性； 3.对产生的声纳数据进行不确定性评估； 4。建立使用三维深度学习的多束声纳噪声识别和错误分类的方法； 5。分析噪声源和检测到的错误，以改善未来的自主系统； 用于培训和测试的多梁声纳数据集将从两艘船只中获取：新不伦瑞克大学调查发射Heron，在不列颠哥伦比亚沿岸工作，加拿大海岸警卫队船Amundsen在整个加拿大北极工作。计算编程，实验设计和现场体验的结合形成了一种沉浸式HQP培训环境，用于不同背景的HQP。计划HQP将介绍三维深度学习算法，多层声纳数据获取和处理协议的最新信息，以及在以海洋映射为中心的工程研究实验室内的包容性培训环境中进行的统计测试。 改善自主调查平台的应用和从传统调查船中删除人员将提高海底映射的效率，限制海洋事故，并降低海底映射的成本，尤其是在偏远地区。只有一小部分加拿大和国际水域映射到现代标准，并提高自动船收集这些关键数据的能力将为加拿大人和世界各地的其他人带来好处。