用太赫兹光谱+深度学习，准确识别12类塑料
Multi-Scale Feature Attention Network for Polymer Classification Using Terahertz Spectroscopy
- 多尺度卷积+注意力机制，自动抓取关键频段特征
- 在12类聚合物上达到85.2%分类准确率，优于现有模型
- 适合材料回收、工业质检等需要快速无损检测的场景
可靠的聚合物识别对确保再生塑料的质量与安全至关重要，但传统分拣和光谱技术常难以实现稳健区分。太赫兹（THz）光谱提供了一种有前景的替代方案，具备高分辨率和非破坏性测量能力。本文利用太赫兹信号对12种聚合物（包括纯聚合物、多层膜、商用混合物和生物聚合物）进行分类。为应对这些光谱信号的复杂性，我们提出多尺度特征注意力网络（MSFAN），一种专为太赫兹数据设计的新型深度学习架构。该框架结合特征门控实现信号重校准，以及多尺度并行卷积以捕捉多样化的频率模式。这些特征通过跨特征注意力和注意力池化进一步优化，使模型能内在地突出最具信息量的太赫兹区域。MSFAN持续优于现有最优模型，在12类聚合物上实现85.2%的分类准确率。本研究展示了将太赫兹光谱与深度学习结合用于高效、可扩展且可解释的聚合物分类的潜力。
原文摘要 · Abstract (English)
Reliable polymer identification is essential for ensuring the quality and safety of recycled plastics, yet conventional sorting and spectroscopic techniques often struggle to deliver robust discrimination. Terahertz (THz) spectroscopy offers a promising alternative, providing high-resolution and non-destructive measurements. In this work, we leverage THz signals to classify 12 types of polymers, including pure polymers, multilayer films, commercial blends, and biopolymers. To handle the complexity of these spectral signals, we propose the Multi-Scale Feature Attention Network (MSFAN), a novel deep learning architecture tailored for THz data. The framework integrates feature gating for signal recalibration and multi-scale parallel convolutions to capture diverse frequency patterns. These features are further refined through cross-feature attention and attention pooling, enabling the model to intrinsically highlight the most informative THz regions. MSFAN consistently outperforms state-of-the-art models, reaching a classification accuracy of 85.2%. This study demonstrates the potential of combining THz spectroscopy with deep learning techniques for effective, scalable, and interpretable polymer classification.
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