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基于深度学习的图像分类算法在家庭垃圾分类中的应用研究.

Jianfei Wang1

  • 1Suzhou Chien-Shiung Institute of Technology, Taicang, 215411, China.

Heliyon
|May 2, 2024
PubMed
概括
此摘要是机器生成的。

通过深度学习自动化家庭废物分类,可显著提高准确性. 这种新的方法通过减少废物分类中的人为错误来提高回收效率和环境保护.

关键词:
卡普奇人搜索算法 卡普奇人搜索算法卷积神经网络是一种卷积神经网络.深度学习是一种深度学习.错误纠正输出代码的输出代码.家庭垃圾分类家庭垃圾分类机器视觉 机器视觉 机器视觉

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科学领域:

  • 计算机科学 计算机科学
  • 环境科学 环境科学
  • 人工智能的人工智能

背景情况:

  • 手动垃圾分类容易出错,并带来环境风险.
  • 自动化废物分类对于有效的回收和保护至关重要.
  • 机器视觉和深度学习为自动化废物分类提供了潜在的解决方案.

研究的目的:

  • 提出一种基于深度学习的新策略,用于家庭废物分类.
  • 提高自动废物分类系统的准确性和效率.
  • 减少与人工废物分类相关的错误和环境风险.

主要方法:

  • 利用深度学习从废物中基于图像的特征提取.
  • 采用卡普奇人搜索算法 (CapSA) 来优化卷积神经网络 (CNN) 的超参数.
  • 实施混合纠错输出代码 (ECOC) 和人工神经网络 (ANN) 模型进行分类.

主要成果:

  • 在TrashNet数据集上达到98.81%的高分类准确率,在HGCD数据集上达到99.01%.
  • 与以前的方法相比,在废物类型检测方面至少有1.46%的改进.
  • 混合模型的有效性随着更多的目标废物类别的增加而增加.

结论:

  • 拟议的深度学习策略对家庭废物分类非常有效.
  • 该方法对废物管理和回收的现实应用具有显著的希望.
  • 该研究证实了所采用的深度学习和混合模型方法的成功.