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使用飞行时间传感器和深度学习,高通量和精确的牛3D扫描

Gbenga Omotara¹, Seyed Mohamad Ali Tousi¹, Jared Decker²

¹Vision-Guided and Intelligent Robotics Laboratory, Electrical Engineering and Computer Science Department, University of Missouri, Columbia, MO 65201, USA.

Sensors (Basel, Switzerland)

|August 29, 2024

概括

一个新的高通量3D扫描系统使用飞行时间 (ToF) 传感器和深度学习准确地测量牛表型. 这项技术为牲畜研究提供了精确的体积和表面积测量.

科学领域:

农业工程农业工程
计算机视觉计算机视觉
动物科学动物科学

背景情况:

准确的表型定型对于畜牧管理和遗传改进至关重要.
目前用于牛表型的现有方法可能耗时且缺乏精度.

研究的目的:

开发和验证高通量3D扫描系统,用于准确测量牛表型.
评估系统在重建牛群几何和计算体积和面积方面的性能.

主要方法:

使用了一系列由嵌入式设备控制的飞行时间 (ToF) 深度传感器.
采用深度学习方法实现了使用RGB和深度数据的3D点云的自动拼接.
进行了双重验证:用已知的物体控制的环境和牛的现实世界测量.

主要成果:

该系统生成高保真度的3D点云和精确的牛体几何网格.
定量验证证实了在受控和现实世界的环境中精确的体积和表面积测量.
证明了该系统处理移动目标的能力,并生产高质量的未养牛重建.

结论:

开发的3D扫描系统为牛表型化提供了一个非侵入性,准确和高吞吐量解决方案.
这项技术具有很大的潜力,可以推进畜牧业的研究和育种计划.
该系统能够提供精确的测量,支持在畜牧业数据驱动的决策.

关键词:

3D表面重建的3D重建牛类扫描仪牛类扫描仪深度学习是一种深度学习.细分化细分化的细分化

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