LAT-BirdDrone:通过混合神经网络增强的低高度小目标轨迹的高精度分类专用数据集
Ming Ke1, Xin Kang1, Zixuan Zhang1
1Lanzhou University of Technology, Lanzhou, China.
Data in brief
|January 8, 2026
概括
研究人员开发了LAT-BirdDrone,这是一个用于分类低海拔微目标轨迹的新数据集. 这一数据集有助于评估无人机和鸟类检测和跟踪的深度学习模型.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 机器人技术 机器人技术 机器人技术
背景情况:
- 现有的数据集主要集中在对象外观上,忽视了小型低高度目标的轨迹分类.
- 需要专门的数据集来评估卷积神经网络 (CNN) 模块对轨迹分类模型的影响.
研究的目的:
- 创建一个专门的数据集,LAT-BirdDrone,用于低海拔微型目标轨迹的分类.
- 为验证CNN模块在轨道分类中的有效性提供数据.
- 支持对安全应用中微型目标检测和跟踪的研究.
主要方法:
- 使用了Hepu Vision Z50IV-CTVC690110-21,100光电子系统进行数据收集.
- 应用了YOLOv11n用于对象检测和轨迹提取,并通过ByteTrack增强了准确性.
- 收集了鸟类和无人机的可见光和红外视频.
主要成果:
- 建立了LAT-BirdDrone,拥有33,262个图像样本和665个轨迹数据片.
- 数据集涵盖了微目标 (≤11pt × 11pt) 的各种条件 (照明,天气,复杂的背景).
- 包括 65 个视频 (15 个无人机, 50 个鸟),平均轨迹长度为 48 .
结论:
- LAT-BirdDrone解决了用于低高度微型目标轨迹分类数据集的稀缺问题.
- 该数据集有助于评估CNN,变压器,LSTM和混合模型.
- 它为低海拔安全应用和优化光电子传感器使用提供了资源.
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