Classification of Polymers Based on the Degree of Their Transparency in SWIR Spectrum

Abstract

Detection, classification and sorting of polymeric particles is a common task required in recycling industry. In the proposed work, an innovative method for detection of polymeric particles and their classification is introduced. The method is based on evaluation of images of polymeric particles, obtained from short-wavelength infrared (SWIR) camera, by convolutional neural network (CNN). Compared to conventionally used spectroscopes or hyper-spectral imaging, this method utilizes single wavelength (1 050 nm) and a degree of polymer transparency serves as the main descriptor. Five different polymers (ABS, ABS-T, Nylon, PETG, PLA) in form of regular blocks (size 15 × 15 × 0.3 mm) were used in the experiment. In total 203 images (size 288 × 288 px) were prepared for CNN training and 67 for testing. Scalable ASP U-Net was tested in 6 combinations and their outputs were compared. According to used intersection over union metrics over all outputs, the topology with 64 filters and depth of 3 exhibited the best results.