Fine metal-rich waste stream characterization based on RGB data: Comparison between feature-based and deep learning classification methods

Abstract

Background: Material compositions in the recycling industry are currently determined by manual sorting, which is time intensive and shows subjective influences. For an automated, sensor-based material flow characterization a particlebased material classification is necessary. Aim: The classification of metal-containing fine-fractions based on RGB images with different machine learning (ML) techniques is investigated on two created datasets A (12,480 images) and B (19,498 images). Method: Two approaches are compared: In approach I, images are firstly pixel- and then object-based classified with six different ML models on three color spaces. In approach II, images are classified by six different convolutional neural networks (CNNs). Results: The classification of dataset A was possible with high accuracy (textgreater 99.8 %) for both approaches and chosen ML algorithms were of minor importance. For dataset B, approach I achieved an accuracy of 78.2 % ± 2.0 %, and chosen ML algo- rithms were of higher importance for object-based classification. In approach II, the best-performing CNN achieved an accuracy of 80.4 % ± 4.2 % and a top-3 score of 94.2 % ± 2.6 %. Conclu- sion: Results from existing studies for coarser particle sizes can be transferred to fine fractions. Further research is needed to improve the classification of dataset B, e. g. by adding instances to less frequent classes and applying deeper CNNs.