用Transformer实现实时检测废旧电路板元件，助力稀有金属回收
Real-Time Detection of Electronic Components in Waste Printed Circuit Boards: A Transformer-Based Approach
- 基于Transformer架构实现快速元件检测与定位
- 在实时性上优于YOLOv8和YOLOv9等主流检测模型
- 适合智能拆解系统研发人员参考
铜、锰、镓及多种稀土元素等关键原材料对电子产业至关重要。为提高单一原材料浓度，便于从废旧印刷电路板（WPCBs）中提取，本文提出一种基于机电系统与人工智能视觉技术协同的可选性拆解方法。论文评估了实时检测变压器（Real-Time DEtection TRansformer）模型在电子元件检测与定位任务中的实时精度表现。尽管变压器最初在自然语言处理与机器翻译领域取得突破，但在本任务中也展现出卓越性能，其检测效果常优于最新的目标检测与定位模型YOLOv8和YOLOv9。
原文摘要 · Abstract (English)
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.
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