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基于神经网络的轻量级绵羊头检测和动态计数方法.

Liang Wang1,2, Bo Hu1, Yuecheng Hou2

  • 1Department of Electronic Engineering, School of Information Science and Engineering, Fudan University, Shanghai 200438, China.

Animals : an open access journal from MDPI
|November 25, 2023
PubMed
概括

这项研究介绍了绵羊头单射击多盒探测器 (SH-SSD),用于准确检测绵羊头部. 该SH-SSD模型达到96.11%的准确性,提高了检测速度,并减少了智能畜牧业的参数.

关键词:
在DeepSort中进行深度排序.在数羊的时候.动态计数计数 动态计数计数改进了 SSD 的性能.

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科学领域:

  • 计算机视觉 计算机视觉
  • 机器学习 机器学习
  • 人工智能的人工智能

背景情况:

  • 准确的目标检测对于各种应用中的精确计数至关重要.
  • 现有方法在实时动物监测的速度和参数效率方面可能面临挑战.

研究的目的:

  • 开发一个高效,准确的深度学习模型来检测羊头.
  • 提高智能畜牧系统中的目标计数能力.

主要方法:

  • 介绍了羊头单枪多盒探测器 (SH-SSD) 模型.
  • 将三重注意力机制集成到MobileNetV3骨干中,以减少参数和提高速度.
  • 利用空间金字塔聚合和网络子中的三重注意力瓶,以改进特征提取.
  • 在网络头部使用脱头模块进行优化预测.

主要成果:

  • 对于绵羊头,SH-SSD模型实现了96.11%的平均检测准确度.
  • 检测指标显著改善,模型参数减少.
  • 当与DeepSort跟踪算法相结合时,实现了高精度的定量统计.

结论:

  • 该SH-SSD模型为羊头检测提供了一个强大的解决方案,具有高精度和高效率.
  • 该模型的性能和部署简单性为智能畜牧业提供了宝贵的技术支持.
  • SH-SSD有助于在自动化畜牧监测和管理方面的进步.