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Distance Measurements by Taping01:18

Distance Measurements by Taping

Tapes are essential in surveying for accurate, durable, and short-distance measurements. Made from lightweight, nylon-coated steel, they offer flexibility and strength for rugged outdoor use. The nylon coating protects against rust and wear, extending the tape's life. Standard lengths, around 30 meters, are marked in meters and millimeters for precision.Surveyors select tapes based on site conditions and accuracy needs. Lightweight, nylon-coated tapes are commonly used for ease of handling and...

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YOLOv8-DMC:通过增强的关键点检测,实现无接触的3D牛体测量.

Zhi Weng1,2,3, Wenwen Hao1,2,3, Caili Gong1,2,3

  • 1College of Electronic Information Engineering, Inner Mongolia University, Hohhot 010021, China.

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概括

本研究介绍了YOLOv8-DMC,这是一种用于准确,非接触的3D牛测量的深度学习模型. 它通过改进的解剖关键点检测和在各种条件下强大的性能来提高精确的牲畜管理.

关键词:
3D点云重建3D点云重建这就是YOLOv8-DMCC.牛的体型测量方法在深度完成完成.关键点检测检测的关键点检测没有接触的牲畜监测.精准畜牧业 精准畜牧业 精准畜牧业 精准畜牧业

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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:

  • 精确的畜牧管理需要准确的,非接触的方法来测量牛体尺寸.
  • 现有的方法在现实世界农业环境中可能缺乏准确性或稳定性.

研究的目的:

  • 开发和验证YOLOv8-DMC,一种轻量级的深度学习模型,用于使用侧视图进行精确的3D牛群测量.
  • 提高在具有挑战性的条件下在牛群中解剖关键点检测的准确性和稳定性.

主要方法:

  • 使用YOLOv8架构与DRAMiTransformer,MHSA-C2f和CASimAM注意模块集成,用于关键点检测.
  • 实施了16个社区的深度完成和过过程,以生成干净的彩色点云.
  • 在超过7000张图像和137头牛的现实RGB-D图像数据集上验证了模型.

主要成果:

  • 实现了高精度,AP@0.5的0.931和AP@[0.50:0.95]的0.868.95的高精度.
  • 在参数和复杂度的最小增加下,与基线相比,精度有2.14%和3.09%的改进.
  • 与手动测量相比,报告的身体高度 (2.43%),部高度 (2.26%),身体长度 (3.65%) 和大炮周长 (4.48%) 的平均相对误差较低.

结论:

  • YOLOv8-DMC为3D牛群测量提供了一种高效准确的解决方案.
  • 该模型对阻塞和照明变化的强度使其适合于现实世界农业.
  • 该系统对边缘设备部署的支持有助于在精密畜牧管理中的实际应用.