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SwinConvNeXt:用于实时垃圾图像分类的融合深度学习架构
B Madhavi1, Mohan Mahanty1, Chia-Chen Lin2
1Department of Computer Science and Engineering, Vignan's Institute of Information Technology, Duvvada, Visakhapatnam, Andhra Pradesh, India.
Scientific reports
|March 7, 2025
概括
本研究介绍了一种先进的深度学习模型,用于有效的废物管理和回收利用. 新模型显著提高了废物分类的准确性,并降低了与现有方法相比的计算成本.
科学领域:
- 环境科学 环境科学
- 计算机科学 计算机科学
- 人工智能的人工智能
背景情况:
- 不充分的废物管理是一个全球性的挑战,传统的分离方法是低效和昂贵的.
- 现有的深度学习模型在废物分类中的准确性和计算效率方面扎,特别是对于视觉上类似的项目.
研究的目的:
- 通过使用先进的计算机视觉和深度学习,开发出复杂和可持续的废物管理系统.
- 克服当前深度学习模型在准确分类各种废物类型方面的局限性.
主要方法:
- 提出了一种新的深度学习模型,将增强的Swin变压器和改进的ConvNeXt与空间注意力机制相结合.
- 利用分层特征提取和移动窗口机制用于全球特征提取,并优化ConvNeXt用于本地特征提取.
主要成果:
- 在垃圾分类数据集上实现了98.97%的准确性,98.42%的精度和98.61%的回忆.
- 由于其轻量级的设计和低计算要求,拟议的模型在最先进的深度学习模型中表现出优越的性能.
结论:
- 开发的深度学习模型为实时废物分类和回收提供了高效准确的解决方案.
- 该模型在识别废物细微细节方面的有效性为改进废物管理策略铺平了道路.
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