HBID24K:在野生动物监测中用于脆弱的胡巴拉和入侵者检测的新基准数据集
Syed Sadaf Ali1, Syed Danish Ali2, Sajid Javed3
1C2PS & KUCARS, Khalifa University, Abu Dhabi, 127788, United Arab Emirates. syed.ali@ku.ac.ae.
Scientific data
|February 10, 2026
概括
一个新的Houbara bustard图像数据集有助于保护工作. 深度学习模型,特别是YOLOv10,在野生动物研究中显示出自动监控和入侵者检测的前景.
科学领域:
- 野生动物保护 野生动物保护
- 深度学习应用程序深度学习应用程序
- 生态监测 生态监测
背景情况:
- 胡巴拉野在生态和文化上具有重要意义,但很脆弱.
- 自动监测对于它们的保护至关重要,但缺乏足够的数据.
- 深度学习对象检测可以识别对的威胁.
研究的目的:
- 为了创建一个全面的数据集,为Houbara bustard研究.
- 在这种情况下,对对象检测进行深度学习模型的基准测试.
- 通过改进的监测工具支持保护工作.
主要方法:
- 收集了 24,318 张摄像机陷图像 (15,070 张 Houbara, 9,248 个入侵者) 的数据集.
- 图像是在2011-2023年期间使用14个摄像机模型收集的.
- 创建了界限框注释,并对10个物体检测模型进行了基准测试.
主要成果:
- 数据集在时间,位置和摄像机模型方面表现出高度的多样性.
- 与其他模型相比,YOLOv10在各种评估指标上表现出卓越的表现.
- 开发的数据集适合研究Houbara和类似的鱼物种.
结论:
- 新的数据集显著推动了胡巴拉鸟的研究和保护.
- 深度学习,特别是YOLOv10,为自动化野生动物监测提供了有效的解决方案.
- 该资源为野生动物研究和保护实践中的更广泛应用提供了基础.
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