Object Detection Routine for Material Streams Combining RGB and Hyperspectral Reflectance Data Based on Guided Object Localization

Abstract

Electronic waste is the fastest growing type of scrap globally and is an important challenge due to its heterogeneity, intrinsic toxicity and potential environmental impact. With an objective of obtaining information on the composition of printed circuit boards (PCBs) through non-invasive analysis to aid in recycling and recovery of precious waste, the goal of this paper is to propose a scheme towards the fusion of RGB and hyperspectral data in object detection. State-of-art detectors come with their own set of challenges which make them inapplicable to PCB recycling. We introduce a method which promises to achieve object detection based on multi-sensor data by utilizing the hyperspectral data to localize components and compare the results to a conventional single-sensor (RGB) based approach.