在深度学习中对预处理和模型压缩技术进行比较研究,用于森林声音分类
Thivindu Paranayapa1, Piumini Ranasinghe1, Dakshina Ranmal1
1Department of Computer Science & Engineering, University of Moratuwa, Moratuwa 10400, Sri Lanka.
Sensors (Basel, Switzerland)
|February 24, 2024
概括
深度学习模型,特别是卷积神经网络 (CNN),可以针对边缘设备进行优化. 压缩技术,如修剪和量化,可以在资源有限的硬件上准确地对森林声音进行分类.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 信号处理 信号处理
背景情况:
- 深度学习模型在复杂的任务中表现出色,但往往需要大量的计算资源.
- 在资源有限的边缘设备上部署先进的AI存在重大挑战.
- 声学数据分析受益于有效的深度学习技术,用于现实世界的应用.
研究的目的:
- 为了比较分析七个卷积神经网络 (CNN) 模型在边缘设备上部署的性能.
- 调查数据增强,特征提取和模型压缩技术的有效性.
- 用森林声音数据集的声学数据来评估CNN模型.
主要方法:
- 对七种不同的CNN架构进行比较分析.
- 应用数据增强和特征提取技术.
- 实施模型压缩策略,包括重量/过器修剪和8位量化.
主要成果:
- 优化的CNN通过压缩实现了准确性和模型大小之间的平衡.
- 移动Net-v3-小和ACDNet显示高精度 (87.95%和85.64%) 与紧的尺寸 (243 KB和484 KB).
- 重量和过器修剪,然后进行8位定量化,证明了模型压缩的有效性.
结论:
- 卷积神经网络可以有效地被压缩和优化,以便在资源有限的边缘设备上部署.
- 该研究表明,使用优化的CNN来实时进行森林环境声音分类的可行性.
- 有效的深度学习模型对于在边缘计算应用中推进AI能力至关重要.
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