这项工作提出了一种使用低成本光谱传感器模块以及一组机器学习方法来识别塑料的方法。传感器是能够测量从可见光到近红外的18个波长的多光谱模块。使用一组十种机器学习方法(随机森林,支持向量机,多层感知器,卷积神经网络,决策树,Logistic回归,天真的贝叶斯,k-最近的邻居,AdaBoost,线性判别分析)。设计了一个实验装置,用于从包括PET在内的六种塑料类型中收集系统的数据,HDPE,PVC,LDPE,PP和PS生活垃圾。在通用管道中实现了一组计算方法,以验证所提出的识别塑料的方法。结果表明,卷积神经网络和多层感知器可以识别塑料,平均准确率为72.50%和70.25%,分别,PS塑料的最大精度为83.5%,PET塑料的最小精度为66%。结果表明,这种低成本的近红外传感器结合机器学习方法可以有效地识别塑料,使其成为一种负担得起的便携式方法,有助于开发可持续系统,并具有在农业等其他领域应用的潜力,电子垃圾回收,医疗保健和制造业。
This work presents an approach for the recognition of plastics using a low-cost spectroscopy sensor module together with a set of machine learning methods. The sensor is a multi-spectral module capable of measuring 18 wavelengths from the visible to the near-infrared. Data processing and analysis are performed using a set of ten machine learning methods (Random Forest, Support Vector Machines, Multi-Layer Perceptron, Convolutional Neural Networks, Decision Trees, Logistic Regression, Naive Bayes, k-Nearest Neighbour, AdaBoost, Linear Discriminant Analysis). An experimental setup is designed for systematic data collection from six plastic types including PET, HDPE, PVC, LDPE, PP and PS household waste. The set of computational methods is implemented in a generalised pipeline for the validation of the proposed approach for the recognition of plastics. The results show that Convolutional Neural Networks and Multi-Layer Perceptron can recognise plastics with a mean accuracy of 72.50% and 70.25%, respectively, with the largest accuracy of 83.5% for PS plastic and the smallest accuracy of 66% for PET plastic. The results demonstrate that this low-cost near-infrared sensor with machine learning methods can recognise plastics effectively, making it an affordable and portable approach that contributes to the development of sustainable systems with potential for applications in other fields such as agriculture, e-waste recycling, healthcare and manufacturing.