Abstract

The Cumberland Sound Beluga is a threatened population of belugas and the assessment of the population is done by a manual review of aerial surveys. The time-consuming and labor-intensive nature of this job motivates the need for a computer automated process to monitor beluga populations. In this paper, we investigate convolutional neural networks to detect whether a section of an aerial survey image contains a beluga. We use data from the 2014 and 2017 aerial surveys of the Cumberland Sound, conducted by the Fisheries and Oceans Canada to simulate two scenarios: (1) when one annotates part of a survey and uses it to train a pipeline to annotate the remainder and (2) when one uses annotations from a survey to train a pipeline to annotate another survey from another time period. We experimented with a number of different architectures and found that an ensemble of 10 CNN models that leverage Squeeze-Excitation and Residual blocks performed best. We evaluated scenarios (1) and (2) by training on the 2014 and 2017 surveys, respectively. In both scenarios, the performance on (1) is higher than (2) due to the uncontrolled variables in the scenes, such as weather and surface conditions.

摘要

坎伯兰湾白鲸是一种受威胁的白鲸种群，种群评估是通过人工审查空中调查完成的。这项工作耗时耗力，因此需要计算机自动化流程来监测白鲸种群。在本文中，我们研究了卷积神经网络以检测航测图像的一部分是否包含白鲸。我们使用加拿大渔业和海洋部在 2014 年和 2017 年对坎伯兰湾进行的航测数据来模拟两种情况：(1) 当一个人对调查的一部分进行注释并使用它来训练管道以对其余部分进行注释时，以及（ 2）当人们使用调查中的注释来训练管道以注释另一个时间段的另一项调查时。我们对许多不同的架构进行了试验，发现利用 Squeeze-Excitation 和 Residual 块的 10 个 CNN 模型的集合表现最好。我们分别通过 2014 年和 2017 年调查的培训评估了情景 (1) 和 (2)。在这两种情况下，由于场景中的不受控制的变量（例如天气和地表条件），(1) 的性能高于 (2)。