自动自行车:基于人工视觉的原型,用于自动化材料分类过程 (塑料,玻璃,纸板和金属)
Anggie P Echeverry1, Carlos F López1
1Faculty of Electronic Engineering, Corporación Universitaria Autónoma del Cauca, Calle 5 Nro. 3-85, Popayán 190003, Colombia.
HardwareX
|September 18, 2024
概括
本研究介绍了一种使用人工视觉和卷积神经网络 (CNN) 来分类可回收材料的自动化废物分类系统. 该系统实现了高精度,有助于全球回收工作和减少环境污染.
科学领域:
- 环境科学 环境科学
- 计算机科学 计算机科学
- 工程 工程师 工程师 工程师
背景情况:
- 全球污染日益增加,需要改善废物管理.
- 固体废物的不充分分类在全球范围内构成了重大环境挑战.
研究的目的:
- 开发用于识别和分离可回收材料的自动化系统.
- 为了利用人工视觉和机器学习进行有效的废物分类.
主要方法:
- 一个使用网络摄像头和Nvidia® Jetson NanoTM的自动分类系统.
- 为废物识别进行训练的卷积神经网络 (CNN) 的实施.
- 塑料,玻璃,纸板和金属的实时分类.
主要成果:
- 该系统在分离塑料时实现了95%的准确性.
- 实现了玻璃和金属分离的96%准确度.
- 在纸板分类中证明了94%的准确性.
结论:
- 开发的系统有效地为回收计划做出了贡献.
- 自动废物分类对减少全球环境污染产生积极影响.
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