Computer vision-based detection of marine vessels in the Canadian Arctic using visual and acoustic data

使用视觉和声学数据对加拿大北极地区的船舶进行计算机视觉检测

基本信息

批准号：
560777-2020
负责人：
BranzanAlbu, Alexandra
金额：
$ 1.46万
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：
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=707252
关键词：
Computer vision based detection marine

项目摘要

We aim to develop a robust and accurate system for the autonomous detection of marine vessels in the western Canadian Arctic using visual and passive acoustics data. Among the potential outcomes are the creation of novel technologies integrating computer vision and acoustic signal processing, such as computational systems that can recognize both regular-sized as well as small (50 pixels or less within images of 921x1080 pixels) vessels under a variety of environmental conditions. From an ecological standpoint, the outcome of this project will provide insights that will enable our partners to assess the influence of increased vessel traffic on local marine mammals such as beluga whales. This project will have a significant impact, since it will provide a cost-efficient and more accurate alternative to the traditional data analysis of acoustic and visual data, which is typically performed manually by environmental scientists. This is a topic of high importance to both Canada and our partner organizations, the Wildlife Conservation Society Canada (WCS) and Environment and Climate Change Canada (ECCC). The geographic region of interest has recently suffered an increase in anthropogenic activities, due to the sea ice reduction. Therefore, in order for our partners to carry out their core missions of informing the public and advocating for wildlife conservation policies and planning, the ecological impacts of marine traffic have to be better quantified, monitored and studied. Our proposal makes a direct contribution towards the quantitative analysis of marine traffic.

我们的目标是开发一个强大而准确的系统，利用视觉和被动声学数据自主检测加拿大西部北极地区的船舶。潜在的成果包括创建集成计算机视觉和声学信号处理的新技术，例如能够在各种环境下识别常规尺寸和小型（921x1080 像素图像中 50 像素或更少）船只的计算系统。状况。从生态角度来看，该项目的成果将提供见解，使我们的合作伙伴能够评估船只交通增加对白鲸等当地海洋哺乳动物的影响。该项目将产生重大影响，因为它将为传统的声学和视觉数据分析提供一种经济高效且更准确的替代方案，而传统的声学和视觉数据分析通常由环境科学家手动执行。对于加拿大以及我们的合作伙伴加拿大野生动物保护协会 (WCS) 和加拿大环境与气候变化协会 (ECCC) 来说，这是一个非常重要的话题。由于海冰减少，感兴趣的地理区域最近遭受了人类活动的增加。因此，为了让我们的合作伙伴履行向公众宣传和倡导野生动物保护政策和规划的核心使命，必须更好地量化、监测和研究海上交通的生态影响。我们的建议对海上交通的定量分析做出了直接贡献。