Robust plastic waste classification using wavelet transform multi-resolution analysis and convolutional neural networks
Robust plastic waste classification using wavelet transform multi-resolution analysis and convolutional neural networks
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DOI：
10.1016/j.compchemeng.2023.108516
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发表时间：
2023-12-09
影响因子：
4.3
通讯作者：
Zavala，Victor M.
中科院分区：
文献类型：
--
作者：
Long，Fei;Jiang，Shengli;Zavala，Victor M.
使用光子上转换的中红外光谱(MIR)在塑料垃圾回收方面提供了与近红外光谱(NIR)相比的优势，包括类似的数据收集速度和检测黑色塑料的能力。然而，高速MIR光谱会受到显著噪声的影响。虽然卷积神经网络(CNN)已被用于噪声MIR光谱的准确分类，但CNN对提取的特征的分析却较少受到关注。在这项研究中，我们分析了由CNN从以每秒200个光谱收集的高速MIR光谱中提取的特征。通过Grad-CAM方法可视化显著特征表明，尽管CNN模型达到了100%的准确率，但由于该模型容易受到噪声干扰，预测并不可靠或不稳健。为了解决这一局限性，我们提出了一种基于小波变换的多分辨率分析(MRA)方法，作为噪声MIR光谱的一种预处理方法。结果表明，MRA重建有效地捕捉了与特征红外峰值相关的特征，使CNN模型能够从噪声MIR光谱中提取信息特征，显著提高了预测的保真度和稳健性。
Mid-infrared spectroscopy (MIR) using photon up-conversion provides advantages over near-infrared spectroscopy (NIR) for plastic waste recycling, including comparable data collection speed and the ability to detect black plastics. However, high-speed MIR spectra suffer from the presence of significant noise. While convolutional neural networks (CNNs) have been utilized for accurate classification of noisy MIR spectra, the analysis of extracted features by the CNN has received less attention. In this study, we analyzed features extracted by a CNN from high-speed MIR spectra collected at 200 spectra per second. Visualizing salient features through the Grad-CAM method revealed that, although the CNN model achieved 100% accuracy, the predictions were not reliable or robust, as the model is susceptible to noise interference. To address this limitation, we propose a wavelet transform-based multi-resolution analysis (MRA) as a preprocessing method for noisy MIR spectra. We show that MRA reconstruction effectively captures features related to characteristic IR peaks, enabling the CNN model to extract informative features from noisy MIR spectra and significantly improves the prediction fidelity and robustness.