深度可回收垃圾分类使用集成并行注意力
Hualing Lin1, Xue Zhang1, Junchen Yu1
1College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310027, China.
Sensors (Basel, Switzerland)
|October 16, 2024
概括
本研究介绍了TrashIVL数据集和用于自动化可回收垃圾分类的集成并行注意模块 (IPAM). IPAM增强了计算机视觉模型,提高了对环境保护的分类精度.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 环境科学 环境科学
- 可持续发展 可持续发展 可持续发展
背景情况:
- 手动垃圾分类是低效和劳动密集型的.
- 现有的计算机视觉模型在垃圾图像分类中与背景干扰作斗争.
- 缺乏多样化,现实世界的垃圾数据集阻碍了模型开发.
研究的目的:
- 引入 TrashIVL 数据集,用于培训和评估可回收垃圾分类模型.
- 设计一种新的注意力模块 (IPAM),以提高对背景噪声的模型稳定性.
- 推进使用计算机视觉进行自动垃圾分类,以提高回收效率.
主要方法:
- 开发了TrashIVL数据集,其中包括5个 (TrashIVL-5) 和12个 (TrashIVL-12) 类,以现实生活背景为特色.
- 关于综合并行注意模块 (IPAM) 的建议,以专注于垃圾的基本特征.
- 将IPAM集成到一个卷积神经网络 (CNN) 中,用于可回收垃圾分类系统.
主要成果:
- 拟议的模型在TrashIVL-5数据集上实现了97.42%的准确性.
- 该模型在更复杂的TrashIVL-12数据集上实现了94.08%的准确性.
- IPAM有效地减轻了背景干扰,提高了排序性能.
结论:
- 垃圾IVL数据集和IPAM为自动可回收垃圾分类提供了一个强大的解决方案.
- 这项工作表明了计算机视觉在环境保护方面的巨大潜力.
- 开发的系统有助于提高回收效率和可持续发展.
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