Computer vision applications are increasingly popular for wildlife monitoring
tasks. While some studies focus on the monitoring of a single species, such as
a particular endangered species, others monitor larger functional groups, such
as predators. In our study, we used camera trap images collected in
north-western New South Wales, Australia, to investigate how model results were
affected by combining multiple species in single classes, and whether the
addition of negative samples can improve model performance. We found that
species that benefited the most from merging into a single class were mainly
species that look alike morphologically, i.e. macropods. Whereas species that
looked distinctively different gave mixed results when merged, e.g. merging
pigs and goats together as non-native large mammals. We also found that adding
negative samples improved model performance marginally in most instances, and
recommend conducting a more comprehensive study to explore whether the marginal
gains were random or consistent. We suggest that practitioners could classify
morphologically similar species together as a functional group or higher
taxonomic group to draw ecological inferences. Nevertheless, whether to merge
classes or not will depend on the ecological question to be explored.

借助摄像机陷阱图像，研究了多个物种合并成单一类别对模型结果的影响，以及添加负样本是否能改善模型性能，并发现合并具有形态相似性的物种有利于模型性能，而与之 distinctively 不同的物种合并则效果不一。添加负样本在大多数情况下稍微改善了模型性能，并建议进行更全面的研究以确定其收益是否一致。建议将形态相似的物种合并成功能组或更高级的分类群，以推断生态关系，但是否合并类别将取决于所探索的生态问题。

比较细粒度和粗粒度的生态目标检测

Comparing fine-grained and coarse-grained object detection for ecology

Deep learning methods for computer vision tasks show promise for automating
the data analysis of camera trap images. Ecological camera traps are a common
approach for monitoring an ecosystem's animal population, as they provide
continual insight into an environment without being intrusive. However, the
analysis of camera trap images is expensive, labour intensive, and time
consuming. Recent advances in the field of deep learning for object detection
show promise towards automating the analysis of camera trap images. Here, we
demonstrate their capabilities by training and comparing two deep learning
object detection classifiers, Faster R-CNN and YOLO v2.0, to identify,
quantify, and localize animal species within camera trap images using the
Reconyx Camera Trap and the self-labeled Gold Standard Snapshot Serengeti data
sets. When trained on large labeled datasets, object recognition methods have
shown success. We demonstrate their use, in the context of realistically sized
ecological data sets, by testing if object detection methods are applicable for
ecological research scenarios when utilizing transfer learning. Faster R-CNN
outperformed YOLO v2.0 with average accuracies of 93.0\% and 76.7\% on the two
data sets, respectively. Our findings show promising steps towards the
automation of the labourious task of labeling camera trap images, which can be
used to improve our understanding of the population dynamics of ecosystems
across the planet.

本文论述了如何通过使用深度学习方法对相机陷阱图像进行自动化数据分析，演示了以 Faster R-CNN 和 YOLO v2.0 作为分类器识别、量化和定位相机陷阱图像中的物种，进而应用于生态研究。结果显示 Faster R-CNN 的分类准确率高达 93.0％，取得了很好的效果。