British Antarctic Survey (BAS) researchers launch annual expeditions to the
Antarctic in order to estimate Antarctic Krill biomass and assess the change
from previous years. These comparisons provide insight into the effects of the
current environment on this key component of the marine food chain. In this
work we have developed tools for automating the data collection and analysis
process, using web-based image annotation tools and deep learning image
classification and regression models. We achieve highly accurate krill instance
segmentation results with an average 77.28% AP score, as well as separate
maturity stage and length estimation of krill specimens with 62.99% accuracy
and a 1.96 mm length error respectively.

英国南极调查队研究人员在南极每年进行考察，以估计南极磷虾生物量并评估与前几年的变化。通过比较，我们开发了自动化的数据收集和分析工具，利用基于网络的图像注释工具和深度学习图像分类和回归模型。我们实现了高度准确的磷虾实例分割结果，平均 AP 得分为 77.28%，并且对磷虾标本的成熟阶段和长度估计的准确性分别为 62.99% 和 1.96 毫米。