一个生态友好的R工作流,用于加快摄像头陷图像的物种级分类
L Petroni1, L Natucci1, A Massolo1,2,3
1Ethology Unit, Department of Biology University of Pisa Pisa Italy.
Ecology and evolution
|December 16, 2024
概括
本研究介绍了一种用户友好的R工作流程,用于使用深度学习进行摄像头陷图像分析. 该系统有效地对图像进行分类,改善了对没有高级编程技能的研究人员的生态数据处理.
科学领域:
- 生态生态学 生态生态学
- 计算机科学 计算机科学
- 野生动物管理 野生动物管理
背景情况:
- 摄像头陷对于生态研究至关重要,但在大型数据集方面面临着挑战.
- 人工智能,特别是深度学习,提供解决方案,但往往需要编程专业知识.
- 现有的用户友好的软件和基于R的深度学习工具提高了生态学家的可访问性.
研究的目的:
- 开发一个可访问,用户友好的R工作流程,以简化摄像头陷图像的选择和分类.
- 整合最先进的深度学习模型 (MegaDetector和YOLOv8) 进行高效的图像分析.
- 为生态学家提供可定制的解决方案,即使计算资源或编程技能有限.
主要方法:
- 使用R脚本开发了一个工作流程,集成MegaDetector用于图像标签和YOLOv8用于自定义分类.
- 整合了离线图像增强技术,以解决不平衡的数据集.
- 存储的推断结果是一个与 Timelapse 兼容的数据库,用于质量控制.
主要成果:
- 实现了高性能指标:0.962精度,0.945回忆,和0.913平均精度与一个小的训练集.
- 定制的YOLOv8模型在物种层面上正确分类了91.8%的图像.
- 对于以90%以上的确定性分类的图像,证明了高可靠性分类 (97.1%).
结论:
- 基于R的工作流显著提高了使用深度学习的摄像头陷数据分析的效率和可访问性.
- YOLO架构允许在资源有限的机器上应用,使高级分析更容易实现.
- 工作流程,包括脚本和模型,是公开的适应和进一步发展在生态研究.
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