使用YOLOv12进行猫头颗粒骨片段分类的深度学习框架
Nik Fadzly1,2,3, Lay Wai Kean4, Siti Nuramaliati Prijono5
1School of Biological Sciences, Universiti Sains Malaysia, USM, Pulau Pinang, 11800, Malaysia. nfadzly@usm.my.
Scientific reports
|August 13, 2025
概括
这项研究引入了一种人工智能工具,用于在猫头颗粒中自动识别动物骨头,改进小型哺乳动物群体监测以保护和害虫管理. 自动化系统提高了生态调查的准确性和效率.
科学领域:
- 生态生态学 生态生态学
- 人工智能的人工智能
- 保护生物学 保护生物学
背景情况:
- 对小型哺乳动物种群的非侵入性监测对于生物多样性保护和农业生态系统中的害虫管理至关重要.
- 谷仓猫头颗粒分析是估计猎物丰富的传统方法,但它是劳动密集型的,需要专家知识来进行骨分类.
研究的目的:
- 开发和验证深度学习框架,用于在猫头颗粒中自动检测和分类动物骨碎片.
- 创建一个人工智能辅助的工作流程,以进行可扩展和高效的动物丰度估计.
主要方法:
- 利用YOLOv12对象检测架构进行自动骨片识别.
- 在978张动物骨头 (头骨,大腿骨,下,阴茎) 的注释图像数据集上训练并验证了模型.
- 开发了一个Python脚本,从骨数推断动物的数量.
主要成果:
- 实现了高检测性能,精度=0.90,回忆=0.90,mAP@0.5=0.984和F1得分=0.97.
- 在马来西亚和印度尼西亚的样本中表现出强大的模型概括性.
- 人工智能工作流大大减少了人为错误,并增加了处理吞吐量.
结论:
- 基于人工智能的方法提高了来自猫头颗粒研究的生态推理的准确性和效率.
- 这种方法可以实现可扩展的动物监测,支持及时的生物多样性评估和害虫监测.
- 该框架为农业生态系统及其他领域的研究人员和土地管理人员提供了有价值的工具.
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