GEN Self-Labeling Object Detector for PCB Recycling Evaluation

Abstract

Waste Printed Circuit Boards (WPCBs) contain many valuable and rare metals found in electronic waste, and recycling these boards can help recover these metals and prevent hazardous elements from harming the environment. However, the diverse composition of PCBs makes it challenging to automate the recycling process, which should ideally be tailored to each PCB’s composition. Computer vision is a possible solution to evaluate WPCBs, but most state-of-the-art models depend on labeled datasets unavailable in the WPCB domain. Building a large and fully labeled WPCB dataset is expensive and time-consuming. In addition, the presence of long-tailed class imbalance, where specific electronic components are significantly more prevalent than others, further complicates the development of accurate detection and classification models. To address this, we propose a new method called GEN Self-Labeling Electronic Component Detector, which utilizes a domain adaptation strategy to train semi-supervised teacher-student models that can handle the lack of fully labeled datasets while mitigating the effects of class imbalance. We also introduce a new version of the Waste Printed Circuit Board Economic Feasibility Assessment (WPCB-EFAv2), which characterizes the PCB’s composition by identifying hazardous components, calculating the density of each component type, and estimating the metals that could be recovered from recycling electrolytic capacitors and integrated circuits. Finally, we present a case study involving six PCBs with different characteristics, from which we estimated that 121 g of metals could be recovered. The most recovered metal (108 g) was aluminum from electrolytic capacitors. This information can help reduce the PCB’s composition uncertainty, leading to more efficient dismantling and cost-effective recycling processes.