基于计算机视觉的无脊椎动物生物量估计
Computer vision-based estimation of small arthropod biomass
AI总结：
本文提出基于计算机视觉的无脊椎动物生物量估计方法，通过线性模型和深度神经网络实现干质量估算，结合自动分类学分类，具有较高的准确性。
摘要：
利用仅图像来估计无脊椎动物生物量的能力有助于扩大定量生物多样性监测的努力。基于计算机视觉的方法有潜力取代手动、耗时且破坏性的干称重过程。我们提出了两种干质量估计方法，除了成像样本外不需要额外的手动工作：使用由成像设备自动计算的新型预测变量拟合线性模型，以及训练一组端到端的深度神经网络进行该任务，使用单视图、多视图和元数据感知的架构。我们提出使用面积和下沉速度作为预测变量。这些可以通过BIODISCOVER双相机系统计算，该系统捕捉样本在乙醇柱中下沉的图像序列。为了本研究，我们收集了一个大规模的干质量测量和图像序列配对数据集来训练和评估模型。我们展示了我们的方法即使在复杂且视觉多样的样本形态下也能估计样本干质量。结合自动分类学分类，我们的方法是一种准确的群体级干质量估计方法，个体的中位百分比误差为10-20%。我们强调了选择适当评估指标的重要性，并鼓励同时使用百分比误差和绝对误差作为指标，因为它们测量不同的属性。我们还探讨了不同优化损失、数据增强方法和模型架构用于训练深度学习模型。
英文摘要：
The ability to estimate invertebrate biomass using only images could help scaling up quantitative biodiversity monitoring efforts. Computer vision-based methods have the potential to omit the manual, time-consuming, and destructive process of dry weighing specimens. We present two approaches for dry mass estimation that do not require additional manual effort apart from imaging the specimens: fitting a linear model with novel predictors, automatically calculated by an imaging device, and training a family of end-to-end deep neural networks for the task, using single-view, multi-view, and metadata-aware architectures. We propose using area and sinking speed as predictors. These can be calculated with BIODISCOVER, which is a dual-camera system that captures image sequences of specimens sinking in an ethanol column. For this study, we collected a large dataset of dry mass measurement and image sequence pairs to train and evaluate models. We show that our methods can estimate specimen dry mass even with complex and visually diverse specimen morphologies. Combined with automatic taxonomic classification, our approach achieves a median individual error of 10%-20% and produces group-level dry mass distributions that do not differ significantly from observed distributions. We highlight the importance of choosing appropriate evaluation metrics, and encourage using both percentage errors and absolute errors as metrics, because they measure different properties. We also explore different optimization losses, data augmentation methods, and model architectures for training deep-learning models.
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