# Vision-Based Sorting Systems for Transparent Plastic Granulate
## 1 Introduction  
Plastic waste is present almost all over the planet and poses serious problems to living organisms and the environment. Such types of waste have decomposition periods in the environment of more than 100 years [1]. Therefore, they should be introduced into the environment as little as possible. Waste plastic decomposes into fragments under five millimeters in size. Such particles are microplastics and are ubiquitous in marine and terrestrial environments, and even in the water we drink [2]. They are also present in other organisms, particularly in marines, which is undesirable. Therefore, plastic waste must be managed correctly to prevent it from reaching the environment [3]. Recycling and reuse are more effective ways to reduce the accumulation of plastic waste, including plastic products that have lost their functionality or have defects from their manufacture. Such products could be of different colors or have some patterns on them, but they can also be transparent. When reusing transparent plastic products, all of the impurities must be removed. Potential impurities have a strong impact on recycled products. They affect their mechanical and visual properties. To this end, sorting machines are used to remove impurities from the mix of used ground plastic [4]. The classification of plastics is essential in the recycling industry, because only in this way can different plastics be separated from one another [5, 6]. The sorting machines are of many configurations and operate on different principles for the detection of recycled materials. There are various techniques for identifying and sorting polymers. Some of these techniques include manual sorting, density separation, electrostatic processes, and various optical systems, including optical inspection using photodiodes or charge-coupled device (CCD) machine vision, near infrared (NIR), ultraviolet (UV), X-ray analysis, and fluorescent light or laser radiation [7]. The most basic non-automated sorting is manual sorting. Manual sorting is prone to error, expensive, tedious, and can be unsafe [7]. Density separation systems are used to separate particles with higher densities than water from buoyant ones. Here, the density of particles must significantly differ [7; 8]. Electrostatic separation systems are used to separate a mixture of plastics that can acquire different charges through triboelectrification. It is not suitable for sorting complex mixtures and the particles must be clean and dry [7; 9]. Optical systems, which are based on color imaging (Visible light--VIS) sensors can separate different plastics, primarily by color [10]. Problems only arise when the difference between the different colors is very small [11]. In that case, more advanced methods should be used. Spectrometer and Hyperspectral Imaging are used with a wavelength above a visible range. The former only captures the point and the latter captures the entire line [4]. The most used ranges are VNIR (Visible and near infrared light; 400-1000 nm) and NIR (Near infrared; 800-2500 nm) [10; 12]. The purity of the material in reuse is related to the quality of the product. A lot of research has been done to separate the various types of plastics, such as acrylonitrile butadiene styrene (ABS), polystyrene (PS), polypropylene (PP), polyethylene (PE), polyethylene terephthalate (PET), and polyvinyl chloride (PVC). These plastics can be separated from each other completely by spectrography [12; 13]. Information on the composition of the material can also be obtained by using spectrometry [14; 15]. For better performance, NIR hyperspectral methods are integrated with artificial intelligence methods [16]. These methods are very effective in separating different materials from one another. When looking for differences in the same material with different colors, these methods are less accurate. X-ray is suitable for the identification of polyvinyl chloride (PVC) from polyethylene terephthalate (PET). X-ray may involve higher system complexity and also health risk [10].