使用人工智能选择合适的平台和工作流程来处理相机陷阱数据
Choosing an Appropriate Platform and Workflow for Processing Camera Trap Data using Artificial Intelligence
摘要：
相机陷阱已经改变了生态学家研究野生动物物种分布，活动模式和种间相互作用的方式。虽然相机陷阱提供了一个具有成本效益的方法来监测物种，数据处理所需的时间可能会限制调查效率。因此，人工智能（AI），特别是深度学习（DL）处理相机陷阱数据的潜力得到了相当大的关注。将DL用于这些应用涉及训练算法，例如卷积神经网络（CNN），以自动检测对象并对物种进行分类。为了克服与训练CNN相关的技术挑战，一些研究团体最近开发了将DL纳入易于使用的界面的平台。我们回顾了四个人工智能平台的关键特征-野生动物洞察（WI），MegaDetector（MD），野生动物图像分类机器学习（MLWIC 2）和保护AI -包括数据管理工具和AI功能。我们还在开源GitBook中提供了R代码，以演示用户如何评估模型性能，并将AI输出纳入半自动化工作流程。我们发现，WI和MLWIC 2的物种分类通常具有较低的召回值（图像中存在的动物通常未被分类为正确的物种）。然而，WI和MLWIC 2分类对某些物种的精确度很高（即，在进行分类时，它们一般是准确的）。MD，它使用更广泛的类别对图像进行分类（例如，“空白”或“动物”），也表现良好。因此，我们的结论是，虽然物种分类器不够准确，自动图像处理，DL可以用来提高效率，接受高置信度值为某些物种的分类或过滤图像包含空白。
英文摘要：
Camera traps have transformed how ecologists study wildlife species distributions, activity patterns, and interspecific interactions. Although camera traps provide a cost-effective method for monitoring species, the time required for data processing can limit survey efficiency. Thus, the potential of Artificial Intelligence (AI), specifically Deep Learning (DL), to process camera-trap data has gained considerable attention. Using DL for these applications involves training algorithms, such as Convolutional Neural Networks (CNNs), to automatically detect objects and classify species. To overcome technical challenges associated with training CNNs, several research communities have recently developed platforms that incorporate DL in easy-to-use interfaces. We review key characteristics of four AI-powered platforms -- Wildlife Insights (WI), MegaDetector (MD), Machine Learning for Wildlife Image Classification (MLWIC2), and Conservation AI -- including data management tools and AI features. We also provide R code in an open-source GitBook, to demonstrate how users can evaluate model performance, and incorporate AI output in semi-automated workflows. We found that species classifications from WI and MLWIC2 generally had low recall values (animals that were present in the images often were not classified to the correct species). Yet, the precision of WI and MLWIC2 classifications for some species was high (i.e., when classifications were made, they were generally accurate). MD, which classifies images using broader categories (e.g., "blank" or "animal"), also performed well. Thus, we conclude that, although species classifiers were not accurate enough to automate image processing, DL could be used to improve efficiencies by accepting classifications with high confidence values for certain species or by filtering images containing blanks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
