Abstract
Near-infrared (NIR) spectroscopy is widely used in laboratory and industrial applications for material classification. While standard spectrometers only allow measurement at one sampling point at a time, NIR Spectral Imaging techniques can identify, in real-time, both the size and shape of an object as well as the material it is made from. The robust classification of materials, such as polymers, is based on their characteristic reflectance spectra. As a sample application, we present the real-time classification of waste polymers in a prototype of an automated industrial sorting facility. Sorting requires the correct material, size and shape of the entire object to be known for reliable separation. In this paper, a method for paper label detection on polymer parts is introduced, aimed at enhancing the classification results by merging connected parts of an object.
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