Real-Time Detection of Electronic Components in Waste Printed Circuit Boards: A Transformer-Based Approach
This addresses the need for efficient recycling in the electronic industry by improving component disassembly, though it is incremental as it applies an existing transformer method to a specific domain.
The paper tackles the problem of detecting and localizing electronic components in waste printed circuit boards for recycling critical raw materials, achieving real-time accuracy that often surpasses state-of-the-art models like YOLOv8 and YOLOv9.
Critical Raw Materials (CRMs) such as copper, manganese, gallium, and various rare earths have great importance for the electronic industry. To increase the concentration of individual CRMs and thus make their extraction from Waste Printed Circuit Boards (WPCBs) convenient, we have proposed a practical approach that involves selective disassembling of the different types of electronic components from WPCBs using mechatronic systems guided by artificial vision techniques. In this paper we evaluate the real-time accuracy of electronic component detection and localization of the Real-Time DEtection TRansformer model architecture. Transformers have recently become very popular for the extraordinary results obtained in natural language processing and machine translation. Also in this case, the transformer model achieves very good performances, often superior to those of the latest state of the art object detection and localization models YOLOv8 and YOLOv9.