Classification of aluminum scrap by laser induced breakdown spectroscopy (LIBS) and RGB + D image fusion using deep learning approaches

Integrating multi-sensor systems to sort and monitor complex waste streams is one of the most recent innovations in the recycling industry. The complementary strengths of Laser-Induced Breakdown Spectroscopy (LIBS) and computer vision systems offer a novel multi-sensor solution for the complex task of sorting aluminum (Al) post-consumer scrap into alloy groups. This study presents two novel methods for fusing RGB and Depth images with LIBS using Deep Learning models. The first method is a single-output model that combines LIBS UNET and two DenseNets in a late fusion framework. The second method is a multiple-output model that uses the structure of the single-output model to enhance learning and avoid overfitting. In particular, the network has two outputs that enable the regularization of the individual sensors. A data set of 773 aluminum scrap pieces was created with two sets of ground truth-values, corresponding to the two envisaged sorting tasks, to train and evaluate the developed models. The first sorting task is separating Cast and Wrought (C&W) aluminum. The second is the division of the post-consumer aluminum scrap into three commercially interesting fractions. The single-output model performs best for separating C&W, with a Precision, Recall, and F1-score of 99%. The multiple-output model performs best for classifying the three selected commercial fractions, with a Precision, Recall, and F-score of 86%, 83%, and 84%, respectively. The presented data fusion method for LIBS and computer vision images encompasses the great potential for sorting post-consumer aluminum scrap. By sorting mixed post-consumer aluminum scrap in alloy groups, more wrought-to-wrought recycling can occur, and quality losses can be mitigated during recycling.