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相关概念视频

Field Application of Global Positioning System01:28

Field Application of Global Positioning System

401
The Global Positioning System (GPS) has become an indispensable tool in fieldwork, offering unparalleled precision and efficiency for surveying, navigation, and infrastructure development. By harnessing signals from a constellation of satellites, GPS receivers determine the location of objects with remarkable speed and accuracy, often completing calculations within a second.Advantages of Modern GPS TechnologyContemporary GPS receivers are designed to meet the practical demands of field...
401
Design Example: Identifying the Locations of Monuments in the Field Using Global Positioning System Device01:30

Design Example: Identifying the Locations of Monuments in the Field Using Global Positioning System Device

455
Surveyors use Global Positioning System (GPS) technology to measure the precise location and elevation of points on Earth. In a recent survey, GPS receivers were used to determine the coordinates and elevations of two park monuments. The process involved careful mission planning, data collection, and correction to ensure accuracy. The survey began with mission planning to identify optimal satellite visibility and minimize Position Dilution of Precision (PDOP). A geodetic control point...
455

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相关实验视频

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A Novel Single Animal Motor Function Tracking System Using Simple, Readily Available Software
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使用动态物体跟踪和识别的物联网智能家禽屠宰系统的开发和实施

Hao-Ting Lin1, Suhendra2,3

  • 1Department of Bio-Industrial Mechatronics Engineering, National Chung Hsing University, 145 Xingda Rd., South Dist., Taichung City 402, Taiwan.

Sensors (Basel, Switzerland)
|August 28, 2025
PubMed
概括

这项研究开发了一种使用YOLO-v4的人工智能驱动的系统,用于人道地屠宰家禽. 智能系统能够准确地识别麻醉和未麻醉的红羽,从而提高动物福利和加工效率.

关键词:
物联网动态跟踪对象图像识别屠杀家禽

更多相关视频

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科学领域:

  • 农业工程
  • 人工智能
  • 动物福利科学

背景情况:

  • 越来越多的人性化和高效的家禽屠宰方法.
  • 传统手工检查和现有的电麻醉方法对特定品种的局限性.
  • 对台湾红羽需要专门的系统.

研究的目的:

  • 实施一个智能,安全和人道的屠宰系统,
  • 整合一个支持物联网的视觉系统,以实时监测和管理家禽麻醉.
  • 通过人工智能驱动的物体识别来提高动物福利和处理效率.

主要方法:

  • 使用YOLO-v4模型用于形状特征提取的动态物体追踪识别系统的开发.
  • 整合物联网模块进行实时监控,基于传感器的分类和云决策.
  • 应用图像放大技术以提高模型性能.

主要成果:

  • 在图像放大后,YOLO-v4模型在识别麻醉的 (99%) 和未麻醉的 (89%) 方面取得了高准确性.
  • 该系统在0.75的IOU值下显示了94%的平均精度 (mAP),并以每秒39的速度处理图像.
  • 对于家禽屠宰的识别,YOLO-v4模型的暂时稳定性优于YOLO-X模型.

结论:

  • 开发的智能屠宰系统是用于家禽产业的实用和可扩展的AI应用.
  • 该系统有效提高了台湾红羽的动物福利和加工效率.
  • 实时监测和基于人工智能的识别对于现代,人道的家禽加工至关重要.