We present a convolutional neural network (CNN) framework for classifying different types of plastic materials that are commonly found in mixed plastic waste (MPW) streams. The CNN framework uses experimental ATR-FTIR (attenuated total reflection-Fourier transform infrared spectroscopy) spectra to classify ten different plastic types. An important aspect of this type of spectral data is that it can be collected in real-time; as such, this approach provides an avenue for enabling the high-throughput characterization of MPW. The proposed CNN architecture (which we call PlasticNet) uses a Gramian angular representation of the spectra. We show that this 2-dimensional (2D) matrix representation highlights correlations between different frequencies (wavenumber) and leads to significant improvements in classification accuracy, compared to the direct use of spectra (a 1D vector representation). We also demonstrate that PlasticNet can reach an overall classification accuracy of over 87% and can classify certain plastics with 100% accuracy. Our framework also uses saliency maps to analyze spectral features that are most informative.

我们提出了一个卷积神经网络 (CNN) 框架，用于对混合塑料废物 (MPW) 流中常见的不同类型的塑料材料进行分类。CNN 框架使用实验性 ATR-FTIR（衰减全反射傅立叶变换红外光谱）光谱对十种不同的塑料类型进行分类。这类光谱数据的一个重要方面是可以实时收集；因此，这种方法为实现 MPW 的高通量表征提供了一种途径。提议的 CNN 架构（我们称之为 PlasticNet）使用光谱的 Gramian 角表示。我们表明，这种二维 (2D) 矩阵表示突出了不同频率（波数）之间的相关性，并显着提高了分类精度，与直接使用光谱（一维矢量表示）相比。我们还证明了 PlasticNet 可以达到超过 87% 的整体分类准确率，并且可以以 100% 的准确率对某些塑料进行分类。我们的框架还使用显着图来分析信息量最大的光谱特征。