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基于区块的压缩传感在深度学习中使用AlexNet进行蔬菜分类
Indrarini Dyah Irawati1, Gelar Budiman2, Sofia Saidah2
1School of Applied Science, Telkom University, Bandung, West Java, Indonesia.
PeerJ. Computer science
|December 11, 2023
概括
本研究介绍了一种使用深度学习的AlexNet模型与压缩传感 (CS) 结合的蔬菜分类方法. 这种方法通过准确识别蔬菜,提高农业应用,同时减少计算需求.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 农业技术 农业技术
背景情况:
- 蔬菜分类对于农业自动化至关重要.
- 深度学习,特别是卷积神经网络 (CNN),提供了先进的图像识别功能.
- 现有的方法可能会面临计算效率和存储方面的挑战.
研究的目的:
- 通过使用AlexNet CNN模型,提出一种优化的蔬菜分类技术.
- 将压力传感 (CS) 与AlexNet集成,以提高效率.
- 在准确性和压缩方面评估拟议方法的性能.
主要方法:
- 利用AlexNet深度学习模型进行蔬菜图像分类.
- 应用压缩传感 (CS) 与离散的等号变换 (DCT) 进行分离,高斯分布用于采样,和直角匹配追求 (OMP) 进行重建.
- 实施了一个基于区块的CS方法,与Alex.Net集成.
主要成果:
- 独立的AlexNet模型实现了98%的最大测试准确度.
- 基于区块的CS和AlexNet方法的组合达到96.66%的最大精度,压缩比为2倍.
- 综合方法在分类四种类型的植物图像方面显示出高性能.
结论:
- 亚历克斯网CNN架构提供了强大的蔬菜图像分类.
- 集成基于区块的压缩传感与AlexNet有效地减少了计算时间和存储空间.
- 与以前的技术相比,拟议的混合方法在农业应用中为蔬菜分类提供了一种优越的方法.
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