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Updated: Jul 23, 2025

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基于时间转移模块的猪的有效攻击性行为识别.

Hengyi Ji1,2, Guanghui Teng1,2, Jionghua Yu2

  • 1College of Water Resources & Civil Engineering, China Agricultural University, Beijing 100083, China.

Animals : an open access journal from MDPI
|July 14, 2023
PubMed
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识别猪的侵略性对于养殖场的福利和利是至关重要的. 这项研究引入了一个与ResNeXt50集成的时间转移模块 (TSM),用于准确地自动检测猪的攻击行为.

科学领域:

  • 动物行为 动物行为
  • 计算机视觉 计算机视觉
  • 机器学习 机器学习

背景情况:

  • 猪的侵略行为会对养殖场的利能力和动物福利产生负面影响.
  • 由于其复杂的空间和时间动态,准确地识别猪侵略是具有挑战性的.

研究的目的:

  • 开发一种有效的方法来自动识别猪的侵略行为.
  • 利用具有时间特征处理能力的深度学习模型.

主要方法:

  • 将时间转移模块 (TSM) 集成到四个二维卷积神经网络 (CNN) 架构 (ResNet50,ResNeXt50,DenseNet201,ConvNext-t) 中.
  • 在新建立的猪攻击识别数据集上对TSM集成模型的评估.
  • 基于准确性,回忆力,精度,F1得分,速度和参数数量的模型性能评估.

主要成果:

  • 在ResNeXt50-T模型中,将TSM整合到ResNeXt50中,证明了识别精度和模型参数之间的最佳平衡.
  • 在测试组中实现了高性能指标:95.69%的准确性,95.25%的回忆,96.07%的精度和95.65%的F1分数.
  • 该ResNeXt50-T模型以29毫秒的速度快速处理数据,具有2298万个参数.

结论:

关键词:
在美国,CNN是CNN.行为识别行为识别行为识别计算机视觉 计算机视觉深度学习是一种深度学习.猪猪猪猪猪猪猪猪猪猪猪猪猪猪猪猪猪猪猪猪猪猪猪猪猪猪猪

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  • 拟议的基于TSM的方法显著提高了猪侵略性行为识别的准确性.
  • 这种方法在智能畜牧业应用中为现实世界行为识别提供了有价值的参考.