基于相机阵列的用于回收的多光谱家用塑料分类
Multispectral Household Plastic Classification for Recycling Using a Camera Array
AI总结：
该研究提出一种基于9台带近红外滤波器相机的多光谱成像分类方法，结合4种梯度提升类分类器，实现7种家用塑料的高精度分类，准确率达86.7%，装置采用现成硬件易复制，可集成至工业分选流水线。
AI中文摘要：
塑料污染已成为自然生态系统中一个长期存在的问题。由于废物管理不足、回收效率有限以及回收成本高昂，环境中的塑料废物对生态和健康构成重大风险。回收需要准确识别聚合物类型，但现有的光学分选系统往往难以区分常见的家用塑料。在这项工作中，我们提出了一种基于多光谱成像系统的新型分类方法，该系统由9台配备近红外带通滤波器的相机组成，旨在区分7种最常见的家用塑料。从生成的多光谱图像中，我们提取光谱指纹并推导诸如特定波长对之间的强度差及其斜率、以及伪彩色图像表示等特征。专用的预处理管道在分类前对数据进行对齐和归一化。我们记录了一个多光谱家用塑料数据库（此https URL），并训练了四种不同的分类器：Gradient Boosting、Extreme Gradient Boosting、Light Gradient Boosting Machine和CatBoost。表现最佳的模型达到了86.7%的分类准确率，计算运行时间为每像素2.603μs，能够高效处理高分辨率图像。整个装置由现成的硬件组件构建而成，这使得复制简单，并可直接集成到工业分选流水线中。
英文摘要：
Plastic pollution has become a persistent problem in natural ecosystems. Driven by insufficient waste management, limited recycling efficiency, and high costs for recycling, plastic waste in the environment poses significant ecological and health risks. Recycling requires accurate identification of polymer types, but existing optical sorting systems often struggle to distinguish common household plastics. In this work, we present a novel classification approach based on a multispectral imaging system consisting of nine cameras equipped with near-infrared bandpass filters. The system is designed to discriminate the seven most common household plastics. From the resulting multispectral images, we extract the spectral fingerprints and derive features such as intensity differences between specific wavelength pairs and their slopes, as well as false-color image representations. A dedicated preprocessing pipeline aligns and normalizes the data before classification. We recorded a multispectral household plastic database (https://github.com/FAU-LMS/MHPM) and trained four different classifiers Gradient Boosting, Extreme Gradient Boosting, Light Gradient Boosting Machine, and CatBoost. The best-performing model achieves a classification accuracy of 86.7%. The computational runtime is 2.603 μs per pixel, enabling efficient processing of high-resolution images. The entire setup is built from off-the-shelf hardware components, which makes replication straightforward and allows direct integration into industrial sorting pipelines.
