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Automated detection and tracking of marine mammals: A novel sonar tool for monitoring effects of marine industry

机译:自动检测和跟踪海洋哺乳动物:用于监测海洋工业效果的新型声纳工具

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Many marine industries may pose acute risks to marine wildlife. For example, tidal turbines have the potential to injure or kill marine mammals through collisions with turbine blades. However, the quantification of collision risk is currently limited by a lack of suitable technologies to collect long-term data on marine mammal behaviour around tidal turbines. Sonar provides a potential means of tracking marine mammals around tidal turbines. However, its effectiveness for long-term data collection is hindered by the large data volumes and the need for manual validation of detections. Therefore, the aim here was to develop and test automated classification algorithms for marine mammals in sonar data. Data on the movements of harbour seals were collected in a tidally energetic environment using a high-frequency multibeam sonar on a custom designed seabed-mounted platform. The study area was monitored by observers to provide visual validation of seals and other targets detected by the sonar. Sixty-five confirmed seals and 96 other targets were detected by the sonar. Movement and shape parameters associated with each target were extracted and used to develop a series of classification algorithms. Kernel support vector machines were used to classify targets (seal vs. nonseal) and cross-validation analyses were carried out to quantify classifier efficiency. The best-fit kernel support vector machine correctly classified all the confirmed seals but misclassified a small percentage of non-seal targets (similar to 8%) as seals. Shape and non-spectral movement parameters were considered to be the most important in achieving successful classification. Results indicate that sonar is an effective method for detecting and tracking seals in tidal environments, and the automated classification approach developed here provides a key tool that could be applied to collecting long-term behavioural data around anthropogenic activities such as tidal turbines.
机译:许多海洋工业可能对海洋野生动物构成急性风险。例如,潮汐涡轮机具有通过与涡轮叶片的碰撞造成伤害或杀死海洋哺乳动物的潜力。然而,碰撞风险的量化目前受到缺乏合适的技术限制,以收集潮汐涡轮机周围的海洋哺乳动物行为的长期数据。声纳提供了跟踪潮汐涡轮机周围的海洋哺乳动物的潜在手段。然而,其对长期数据收集的有效性受到大数据卷的阻碍,并且需要手动验证检测。因此,这里的目的是在声纳数据中为海洋哺乳动物进行自动分类算法。关于港口密封件的动作的数据在一个整个设计的海床安装平台上使用高频多沟声纳在整个高频多沟声纳收集。该研究区域由观察者监测,以提供由声纳检测到的密封件和其他目标的视觉验证。声纳检测到六十五个确认的密封和96个其他目标。提取与每个目标相关联的运动和形状参数,并用于开发一系列分类算法。内核支持向量机用于分类目标(密封与非源区)和交叉验证分析来量化分类器效率。最适合的内核支持向量机正确分类所有确认的密封件,但错误分类了少量的非密封目标(类似于8%)作为密封件。形状和非光谱运动参数被认为是实现成功分类中最重要的。结果表明,声纳是用于检测和跟踪潮汐环境中的密封的有效方法,并且在此开发的自动分类方法提供了一种关键工具,其可以应用于围绕诸如潮汐涡轮机的人为活动的长期行为数据。

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