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相关概念视频

Fertilization01:38

Fertilization

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During fertilization, an egg and sperm cell fuse to create a new diploid structure. In humans, the process occurs once the egg has been released from the ovary, and travels into the fallopian tubes. The process requires several key steps: 1) sperm present in the genital tract must locate the egg; 2) once there, sperm need to release enzymes to help them burrow through the protective zona pellucida of the egg; and 3) the membranes of a single sperm cell and egg must fuse, with the sperm...
70.9K

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相关实验视频

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A Neural Network-Based Identification of Developmentally Competent or Incompetent Mouse Fully-Grown Oocytes
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FEDM:一个基于卷积神经网络的受精卵检测模型.

Z Gong1, M Wang1, J Song1

  • 1College of Information Science and Technology, Nanjing Forestry University, Nanjing, China.

British poultry science
|June 3, 2024
PubMed
概括

一个新的AI模型,FEDM,使用YOLOv5准确识别受精的蛋,提高了检测率,并为家禽业提供了具有成本效益的解决方案. 这项技术提高了化成功和经济价值.

科学领域:

  • 农业科学 农业科学
  • 计算机视觉 计算机视觉
  • 人工智能的人工智能

背景情况:

  • 在全球范围内,蛋生产在经济上具有重要意义,受精蛋的质量对于家禽业的成功至关重要.
  • 目前的受精卵识别方法成本高昂,环境要求严格,阻碍了广泛采用.
  • 需要一个低成本,可扩展的识别受精卵的系统来支持家禽业.

研究的目的:

  • 开发一种改进的,低成本的物体检测模型,用于识别适合大规模应用的受精蛋.
  • 增强现有的对象检测模型的特征选择,检测能力和表示能力.
  • 为了简化损失函数的计算过程,以提高效率.

主要方法:

  • 基于YOLOv5架构开发了一个增强的物体检测模型FEDM.
  • 通过SENet注意力机制和C3_DCNv3改进了YOLOv5网络,以更好地选择特征和检测血管.
  • 集成Dyhead来提高对象检测头的容量,MPDIoU取代了原来的损失函数.

主要成果:

  • 在增强数据集上,FEDM模型实现了96.7%的平均精度,比YOLOv5s.s.提高了5.5%.
  • 与YOLOv5.5相比,FEDM在各种拍摄角度中展示了卓越的检测性能,并保持了较高的检测速度.
关键词:
C3_DCNv3 在线阅读粉红色的头发 粉红的头发受精卵检测检测受精卵的检测方法MPDIoUU 的意思是这就是SENet的意义.这是YOLOv5的.

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  • 在使用FEDM检测第四天受精卵的检测率中观察到显著的进步.

    • 结论:

    • 该FEDM模型为识别受精蛋提供了具有成本效益和效率的解决方案,解决了当前方法的局限性.
    • 改进后的模型通过节省和优化大规模测试中的空间利用来提供实际应用价值.
    • 提高FEDM的准确性和速度有助于全球家禽产业的持续发展和经济价值.