Learning from synthesized data for quality assurance in open-source microcontroller manufacturing

Abstract

The proliferation of Arduino has led to numerous low-cost replicas, complicating defect detection due to style variability. Existing detectors struggle to generalize with synthetic data. To address this, we introduce Context-Guided Triplet Attention YOLO-Faster (CGTA-YOLO-F), a real-time model that enhances feature extraction through CGTA blocks, along with a novel C2f-FCGA block (Faster Context Guidance with simplified Attention) for enhancing multi-scale feature fusion. Trained on synthesized data and tested on real data, the method achieves 97.4% mean average precision (mAP) for component detection, outperforming YOLOv8 and YOLOv10 by 3% and 3.4%. It also achieves 91.4% accuracy for misalignment classification, 7.1% higher than the baseline. The model performs well on two additional datasets and integrates detection and classification into a unified framework. It is efficient in speed and memory, making it practical for industrial defect detection tasks.