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The Automated Sorting Methods Modernization of Municipal Solid Waste Processing System

机译:城市生活垃圾处理系统的自动分类方法现代化

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In this paper the plastic sorting system of the waste processing line is considered. Plastic is a valuable raw material for recycling but only 10% of plastic waste is processed nowadays in Russia. Two main methods of waste recognition are applied in two separation stages: spectrometry and computer vision. The implementation of municipal solid waste sorting system using the near infrared and visible spectrum spectrometers can significantly increase the efficiency of separation process in comparison with manual sorting. But the quality of separation is still low because this plastic cannot be separated by color using this method and a lot of impurities are included. The evident conclusion is the need for two-stage of sorting process: the rough but fast sorting using spectrometry and fine sorting using optical methods. Another issues of this paper are related to optical recognition subsystem: we propose a fast multi-level recognition system, the first level is the detection of the object presence on the line and the analysis of camera focusing, the second is the classification of the plastic object using the joint solution of several neural networks. This approach helps to decrease the total operating time by estimating the necessity of neural network running and by avoiding the blurred and faulty image processing.
机译:在本文中,考虑了废物处理线的塑料分选系统。塑料是一种有价值的回收原料,但现在只在俄罗斯加工了10%的塑料废物。两种主要的废物识别方法应用于两个分离阶段:光谱和计算机视觉。使用近红外和可见光谱光谱仪的市政固体废物分选系统的实施可以显着提高与手动分选相比分离过程的效率。但是分离质量仍然很低,因为这种塑料不能使用这种方法与颜色分开,并且包括大量杂质。明显的结论是需要两级分拣过程:使用光谱法使用光谱法和精细分选进行粗糙但快速排序。本文的另一个问题与光学识别子系统有关:我们提出了一种快速的多级识别系统,第一级是检测对象存在的线路和摄像机聚焦的分析,第二是塑料的分类对象使用几个神经网络的联合解决方案。这种方法有助于通过估计神经网络运行的必要性以及避免模糊和故障的图像处理来减少总操作时间。

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