基于YOLO的细分数据集用于无人机与鸟类检测,用于深度和机器学习算法
Shishir Kumar Shandilya1, Aditya Srivastav1, Kyrylo Yemets2
1School of Computing Science and Engineering, VIT Bhopal University, India.
Data in brief
|August 23, 2023
概括
一个新的无人机和鸟类图像数据集有助于开发更好的检测系统. 该资源有助于训练模型准确识别无人机 (UAV) 并减少虚假报警,提高安全.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 机器人技术 机器人技术 机器人技术
背景情况:
- 越来越多地使用无人驾驶飞行器 (UAV) 引发了安全问题.
- 现有的无人机探测系统经常失败或错误地分类像鸟类这样的物体.
- 一个标准化的数据集对于训练强大的无人机检测模型至关重要.
研究的目的:
- 为无人机检测模型培训提供无人机和鸟类图像的新型数据集.
- 为无人机领域的研究人员和开发人员提供全面的资源.
- 提高自动无人机检测系统的准确性和可靠性.
主要方法:
- 使用Roboflow软件与人工智能辅助注释创建数据集.
- 手动,边缘到边缘的图像细分用于详细的训练数据.
- 包含用于模型评估的培训和测试套件.
主要成果:
- 一个数据集,包含各种环境中的无人机和鸟类的各种图像.
- 详细的注释使像YOLO这样的模型能够进行高精度的训练.
- 通过包括常常混的对象,在现有数据集上证明了优势.
结论:
- 这一数据集是推动无人机检测和分类的宝贵资源.
- 它的综合性和各种场景提高了模型培训的有效性.
- 这项工作有助于提高专业和娱乐UAV使用中的安全性和安全性.
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