Robust identification of polyethylene terephthalate (PET) plastics through Bayesian decision.
Robust identification of polyethylene terephthalate (PET) plastics through Bayesian decision.
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DOI：
10.1371/journal.pone.0114518
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发表时间：
2014
期刊：
影响因子：
3.7
通讯作者：
Ramli S
中科院分区：
文献类型：
--
作者：
Zulkifley MA;Mustafa MM;Hussain A;Mustapha A;Ramli S
回收是环境友好型废物管理的最有效方法之一。在城市废物中，塑料是最常见的材料，可以很容易地回收和聚对苯二甲酸乙二醇酯（PET）是其主要类型之一。PET材料用于消费品包装，如饮料瓶，化妆品容器，食品包装等等。通常，回收过程是针对特定材料定制的，以实现最佳的净化和去污，从而获得高等级的可回收材料。分拣过程的数量和质量受到遭受疲劳和无聊的人类工人的能力的限制。在文献中已经提出了几种自动分拣系统，包括使用化学传感器、接近传感器和视觉传感器。基于视觉的传感器的主要优点是其环境友好的方法，非侵入式检测和高通量的能力。然而，现有的方法严重依赖于确定性方法，这使得它们不太准确，因为PET塑料废物外观的变化太大。我们提出了一种通过分析PET材料的反射区域及其周围的概率建模方法。三个参数的高斯分布和指数分布建模：颜色，大小和距离的反射区域。最后通过似然比检验的监督训练方法进行分类。所提出的方法的主要新奇是概率的方法，在整合各种PET材料的签名，污染的污渍下不断的照明变化。该系统通过使用四个性能指标进行评估：精确度，召回率，准确度和错误。与基准方法相比，我们的系统在所有评估指标中表现最好。通过在决策中融合所有邻域信息以及通过在图形处理单元中实现该系统以获得更快的处理速度，可以进一步改进该系统。
Recycling is one of the most efficient methods for environmental friendly waste management. Among municipal wastes, plastics are the most common material that can be easily recycled and polyethylene terephthalate (PET) is one of its major types. PET material is used in consumer goods packaging such as drinking bottles, toiletry containers, food packaging and many more. Usually, a recycling process is tailored to a specific material for optimal purification and decontamination to obtain high grade recyclable material. The quantity and quality of the sorting process are limited by the capacity of human workers that suffer from fatigue and boredom. Several automated sorting systems have been proposed in the literature that include using chemical, proximity and vision sensors. The main advantages of vision based sensors are its environmentally friendly approach, non-intrusive detection and capability of high throughput. However, the existing methods rely heavily on deterministic approaches that make them less accurate as the variations in PET plastic waste appearance are too high. We proposed a probabilistic approach of modeling the PET material by analyzing the reflection region and its surrounding. Three parameters are modeled by Gaussian and exponential distributions: color, size and distance of the reflection region. The final classification is made through a supervised training method of likelihood ratio test. The main novelty of the proposed method is the probabilistic approach in integrating various PET material signatures that are contaminated by stains under constant lighting changes. The system is evaluated by using four performance metrics: precision, recall, accuracy and error. Our system performed the best in all evaluation metrics compared to the benchmark methods. The system can be further improved by fusing all neighborhood information in decision making and by implementing the system in a graphics processing unit for faster processing speed.
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影响因子：
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作者：
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