Real-Time Detection of Electronic Components in Waste Printed Circuit Boards: A Transformer-Based Approach
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 5 of its 12 references that are in the map. First posted on arXiv 24 Sep 2024.
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5 of 12 references.
References · 5
- 2020End-to-End Object Detection with Transformerscited by 7,414
- 2023DETRs Beat YOLOs on Real-time Object Detectioncited by 533
- 2023A Comprehensive Review of YOLO Architectures in Computer Vision: From YOLOv1 to YOLOv8 and YOLO-NAScited by 151
- 2024Measuring the Recyclability of Electronic Components to Assist Automatic Disassembly and Sorting Waste Printed Circuit Boardscited by 1
- 2024Virtual Mines -- Component-level recycling of printed circuit boards using deep learningcited by 1
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- 2026UniPCB: A Unified Vision-Language Benchmark for Open-Ended PCB Quality Inspection
- 2024DocEDA: Automated Extraction and Design of Analog Circuits from Documents with Large Language Model
- 2026SINA: A Fully Automated Circuit Schematic Image to Netlist Generator Using Artificial Intelligence
- 2025Anomaly Object Segmentation with Vision-Language Models for Steel Scrap Recycling
- 2026Symbol and Footprint Database for Electronic Components by Agentic Recognition and Generation
- 2025Visual Language Model as a Judge for Object Detection in Industrial Diagrams
- 2024Preliminary Investigations of a Multi-Faceted Robust and Synergistic Approach in Semiconductor Electron Micrograph Analysis: Integrating Vision Transformers with Large Language and Multimodal Models
- 2026SINA: A Circuit Schematic Image-to-Netlist Generator Using Artificial Intelligence