Automatic multistage classification system for plastic bottles recycling
Automatic multistage classification system for plastic bottles recycling
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DOI：
10.1016/j.resconrec.2007.03.008
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发表时间：
2007-12-01
影响因子：
13.2
通讯作者：
Al-Ali, A. R.
中科院分区：
文献类型：
--
作者：
Tachwali, Y.;Al-Assaf, Y.;Al-Ali, A. R.
在这项工作中，提出、开发并测试了一种人工智能塑料瓶分类系统。尝试根据化学成分和颜色对瓶子进行分类。近红外 (NIR) 反射率测量用于识别瓶子的成分类别。使用融合了二次判别分析（QDA）和树分类器的电荷耦合器件（CCD）相机来检测瓶子颜色。结果表明，反射近红外光谱的浸入波长和平均值可以用作区分化学成分的特征。结果分类准确率为 94.14%。除了各种预处理技术外，使用主成分分析算法进行瓶子定位有助于检测瓶子颜色，避免与瓶子的标签或瓶盖混合。利用所提出的方法，透明瓶子的颜色分类准确率达到 92%，而不透明瓶子的颜色分类准确率达到 96%。组合系统的总体分类准确率（即颜色和化学成分的准确分类）为83.48%。 (C) 2007 Elsevier B.V. 保留所有权利。
In this work, an artificial intelligent plastic bottles classification system is proposed, developed and tested. Classifying bottles based on their chemical composition and color is attempted. Near infrared (NIR) reflectance measurements are used to identify bottle composition class. Charged coupled device (CCD) camera with the fusion of quadratic discriminant analysis (QDA) and tree classifiers are used to detect the bottle color.Results have shown that the dip wavelength and average values of the reflective NIR spectrum could be used as features to distinguish between chemical compositions. This resulted in 94.14% classification accuracy. In addition to various preprocessing techniques, the use of principal component analysis algorithm for bottle orientation facilitates the detection of the bottle color avoiding mixing it with the bottle's label or cap. Ninety-two percent color classification accuracy is achieved for clear bottles while 96% is achieved for opaque one, with proposed method. The aggregate classification accuracy of the combined system (i.e. accurate classification of color as well as chemical composition) is 83.48%. (C) 2007 Elsevier B.V. All rights reserved.