机器视觉技术在低成本设备中的应用,以提高精准农业的效率
Juan Felipe Jaramillo-Hernández1,2, Vicente Julian1,2, Cedric Marco-Detchart1
1Valencian Research Institute for Artificial Intelligence, Universitat Politècnica de València (UPV), Camí de Vera s/n, 46022 Valencia, Spain.
Sensors (Basel, Switzerland)
|February 10, 2024
概括
这项研究引入了一种新的计算机视觉方法,用于用深度估计检测物体,称为深度物体探测器 (DOD). 它为边缘设备上的实时应用提供了精度和速度的高效平衡.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 分布式工作和开源资源的进步正在推动计算机视觉领域的创新.
- 计算机视觉使机器能够解释和与视觉世界互动.
- 对象检测和深度估计对于许多人工智能应用至关重要.
研究的目的:
- 开发和实施一种新的计算机视觉和人工智能方法,用于用集成深度估计进行对象检测.
- 创建一个高效的系统,适合嵌入式和边缘设备上的实时应用.
- 评估与最先进的模型对比拟的方法的性能.
主要方法:
- 深度物体探测器 (DOD) 方法的构想,设计,实施和运行.
- 国防部的训练和评估使用了微软的"环境中的共同对象" (COCO) 数据集和"明尼果"数据集.
- 与当前最先进的物体检测模型进行了基准测试.
主要成果:
- 国防部的方法证明了在嵌入式系统上运行的效率.
- 该系统在准确性和速度之间取得了有利的平衡.
- 结果表明它适用于边缘设备上的实时应用.
结论:
- 拟议的深度物体探测器 (DOD) 是一种高效的计算机视觉方法,用于用深度估计检测物体.
- 国防部的性能使其非常适合物联网 (IoT) 环境中的实时应用.
- 该方法为需要对象识别和空间意识的任务提供了实用解决方案.
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