Martinez-Hernandez Uriel, West Gregory, Assaf Tareq
Department of Electronic and Electrical Engineering, University of Bath, Bath BA2 7AY, UK.
Multimodal Interaction and Robot Active Perception (Inte-R-Action) Lab, University of Bath, Bath BA2 7AY, UK.
Sensors (Basel). 2024 Apr 28;24(9):2821. doi: 10.3390/s24092821.
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.
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。该传感器是一个多光谱模块，能够测量从可见光到近红外的18个波长。数据处理和分析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、逻辑回归、朴素贝叶斯、k近邻、自适应增强、线性判别分析）来进行。设计了一个实验装置，用于从包括PET、HDPE、PVC、LDPE、PP和PS在内的六种塑料类型的家庭垃圾中系统地收集数据。这组计算方法在一个通用管道中实现，以验证所提出的塑料识别方法。结果表明，卷积神经网络和多层感知器分别能够以72.50%和70.25%的平均准确率识别塑料，其中PS塑料的最大准确率为83.5%，PET塑料的最小准确率为66%。结果表明，这种结合机器学习方法的低成本近红外传感器能够有效地识别塑料，使其成为一种经济实惠且便于携带的方法，有助于可持续系统的发展，并有可能应用于农业、电子垃圾回收、医疗保健和制造业等其他领域。
