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黑色塑料通过中波红外光中高光谱成像进行识别

Lukas Roming¹, Felix Kronenwett¹, Paul Bäcker¹

¹Fraunhofer IOSB, Institute of Optronics, System Technologies and Image Exploitation, Fraunhoferstraße 1, Karlsruhe, 76131, Baden-Württemberg, Germany.

Waste management (New York, N.Y.)

|October 9, 2025

概括

中波红外 (MWIR) 超光谱成像有效分类黑色塑料,优于近红外 (NIR) 方法. 这一进步对于回收黑色塑料废物至关重要,而目前的NIR技术往往忽略了这一点.

科学领域:

材料科学材料科学材料科学
分析化学分析化学
环境科学环境科学

背景情况:

基于近红外 (NIR) 传感器的分类是消费后塑料的标准.
由于碳黑色素的吸收,NIR与黑色塑料作斗争.
中波红外线 (MWIR) 为分析黑色聚合物提供了另一种波长范围.

研究的目的:

为了比较MWIR与NIR高光谱成像用于塑料废物分类的有效性.
为了评估黑色和彩色塑料废物的性能.
评估各种化学测量方法,包括卷积神经网络 (CNN).

主要方法:

收集了五种常见聚合物的光谱数据:HDPE,LDPE,PET,PP和PS.
使用MWIR和NIR高光谱成像技术.
应用化学测量方法,特别是CNN,用于光谱分类.

主要成果:

与NIR (47.5%) 相比,MWIR在黑色塑料分类方面表现优异 (83.4%的准确性与CNN平衡).
对于有色塑料,NIR显示出轻微的优势,平衡精度比MWIR高7个百分点.
在所有传感器和样本类型中,CNN的性能优于其他化学测量方法.

结论:

MWIR高光谱成像是一种高效的NIR替代品,用于对塑料垃圾进行分类,特别是在高黑色塑料场景中.
这项技术可以显著提高黑色塑料包装的回收率.
这些发现支持将MWIR成像集成到废物管理基础设施中.

关键词:

黑色的塑料是黑色的塑料.卷积神经网络是一种卷积神经网络.超光谱成像技术的使用.机器学习机器学习中波红外线是中波红外线.接近红外的近红外.塑料的回收利用塑料的回收利用聚合物识别聚合物识别垃圾分类垃圾分类垃圾分类

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