# Vision-Based Sorting Systems for Transparent Plastic Granulate
## 1 Introduction
Laser technique offers less-than-a-microsecond fast identification of plastic based on atomic emission spectroscopy. A laser is used to release excited ions and atoms from the material surface, and these can then be identified through spectral analysis to provide polymer type and additives present [7; 17; 18]. Sorters based on CCD cameras and machine vision occur mainly in sorters of various objects. Support vector machines and artificial neural networks are popular choices for classifiers. Thus, a sorter was developed that separates various objects, such as gears, coins, and connectors [19]. Methods of machine vision, such as deep learning, are emerging in waste sorting. With such methods it is possible to classify and detect various objects, such as glass bottles, paper boxes, paper cups, ceramic plates, and so on [20]. Most of the methods of machine vision in sorting occur in food production. Thus, various defects on crops, such as tomatoes, oranges, lemons, eggs, seeds, and almonds, are inspected with deep learning [21; 22; 23], support vector machines [24], neural networks [25], and \(k\)-Nearest Neighbors [26]. These methods are also used in the processing of foods, such as fish and chicken. The quality is checked or certain parts of the animal are identified [27; 28]. There are also simpler methods for checking seed coloration by counting pixels of a particular color [29]. Researching the sorting machines, it was found that there were different manipulators for the physical separation of particles. These are various pneumatic, electrical, and hydraulic manipulators for sorting larger objects. Ejecting with compressed air and pneumatic nozzles for smaller objects is also widely used [30; 31; 32]. There are also advanced algorithms for tracking and simulating single particle excretion. These algorithms are able to track objects on the conveyor belt and separate them from other objects with great precision [33; 34; 35]. In this research, the development of a real-time sorting machine for sorting transparent plastic granulate with a lot of different defects was presented. These defects are different black dots, blurs, burns, etc. These products are unusable. This can be done by turning the product into base material for new plastics. In the recycling process, defects caused by product injection molding must be eliminated. For this purpose, a polycarbonate (PC) granulate sorting machine was developed, which removes particles with these defects. The developed sorting machine made an optical control of the granules with the help of a camera operating in the visible range of light. The particles needed to be inspected, so machine vision was used. Machine vision searched for each individual granule in the image and classified it using the _k_-Nearest Neighbors (_k_-NN) method. This method was chosen based on the datatype and application requirements. The images to be processed were of size (40 \(\times\) 40 pixels), which is very small in comparison to images used in conventional machine vision applications. Granule classification requires real-time processing, so a fast and efficient algorithm is needed. The physical sorting of the granules was performed with air nozzles. Existing sorters offer the sorting of particles by color and size, and some can even determine the composition of the material the particle is made of. The developed prototype sorter uses machine vision and artificial intelligence methods. With these methods, each individual transparent particle can be classified according to the training database so one can determine whether they are to be ejected. The sorting machine can check their color and any irregularities that may appear in a particle.