Real-Time Detection of Electronic Components in Waste Printed Circuit Boards: A Transformer-Based Approach

TL;DR

This paper presents a transformer-based model for real-time detection and localization of electronic components on waste printed circuit boards, aiming to facilitate the extraction of critical raw materials.

Contribution

It introduces a transformer architecture tailored for real-time electronic component detection, outperforming current state-of-the-art models like YOLOv8 and YOLOv9.

Findings

Transformer model achieves high accuracy in component detection.

Outperforms YOLOv8 and YOLOv9 in real-time detection tasks.

Supports efficient disassembly for raw material recovery.

Abstract

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.