Learning from pseudo-labels: Self-training Electronic Components Detector for Waste Printed Circuit Boards

• Agostinho A. F. Júnior UPE
• Leandro H. de S. Silva UPE / IFPB
• Bruno J. T. Fernandes UPE
• George O. A. Azevedo UPE
• Sérgio C. Oliveira UPE

Resumo

Electronic components (EC) detection is relevant for Printed Circuit Board (PCB) manufacturing, quality inspection, and assurance (cyber-security). Moreover, at end-of-life electronic devices, the PCBs become Waste PCBs (WPCBs), the primary source of high-value materials in electronic waste. However, the recycling process is challenging because of the WPCB’s high composition diversity. EC detection in WPCBs can reduce the uncertainty about WPCBs composition and help select a better recycling process. Nevertheless, large fully-annotated datasets for the PCB domain are available at a high cost. For WPCBs, there is only one publicly available dataset, the PCB-DSLR, and it is partially labeled. As for PCBs, the FICS-PCB dataset is available. However, it contains images of modern and clean PCBs, all in good condition, while the first has broken and dirty parts. Thus, we propose a self-training strategy for object detection algorithms without needing a fully labeled dataset. In the strategy, the YOLOv5 model is used to detect the eight types of electronic components present in FICS-PCB in PCB-DSLR, which initially has labels only for integrated circuits. The process occurs through the interaction between a teacher model, trained with the FICSPCB data, and a student model, which is trained by joining the FICS-PCB data and the teacher-generated labels for PCB-DSLR. The results show that the student model performs better in the electronic components detection task, obtaining greater precision and sensitivity when compared to the teacher model. Since we are dealing with partially labeled data, we provide a low-dimensional representation of the detections of both models using t-SNE.

Palavras-chave: Uncertainty, Sensitivity, Printed circuits, Electronic components, Detectors, Object detection, Data models, Self-training, Electronic components detector, Teacher-student models