使用机器学习和边缘计算进行花岩的分类.
Madhavi Karanam1, Krishna Chythanya Nagaraju2, Gotham Sai P3
1Professor,HoD,CSE,, Gokaraju Rangaraju Institute of Engineering and Technology, Hyderabad, Telangana, 500090, India.
F1000Research
|November 29, 2023
概括
一个新的机器学习花岩分类器使用边缘计算和颜色传感器来准确识别岩石. 该系统有助于消费者区分花岩类型,防止商家欺诈,确保他们收到所选石头.
科学领域:
- 材料科学 材料科学 材料科学
- 计算机科学 计算机科学
背景情况:
- 室内装饰和氛围在很大程度上依赖于花岩等设计材料.
- 需要一个系统来帮助消费者区分花岩类型,并防止商家欺诈性替代.
- 使用卷积神经网络 (CNN) 和图像处理的现有解决方案缺乏精度和计算效率.
研究的目的:
- 开发一个精确且计算效率高的花岩分类系统.
- 为了帮助最终用户选择补充其家居装饰的花岩.
- 通过确保商家提供消费者选择的精确花岩颜色来防止欺诈行为.
主要方法:
- 开发了一个基于机器学习的花岩分类器,利用边缘计算.
- 集成了一个色彩传感器 (TCS3200) 与ESP8266板,以从岩石中获取RGB数据.
- 训练了一种机器学习算法,特别是随机森林,使用获得的颜色数据进行花岩的分类.
主要成果:
- 该系统在分类花岩类型时达到94%的准确性.
- 开发的系统为区分各种花岩石提供了一种可靠的方法.
- 边缘计算集成确保了实时分类的计算效率.
结论:
- 拟议的系统使最终用户能够自信地区分不同的花岩类型.
- 这项技术为商业交易中验证花岩真实性的实际解决方案.
- 该系统提高了消费者对花岩的选择和购买的信任和满意度.
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