Learning from synthesized data for quality assurance in open-source microcontroller manufacturing
Year: 2025
Type: article
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.
Source: Measurement
Authors: Zhifan Song, Abd Al Rahman M. Abu Ebayyeh
Language: English
Subfield: Industrial and Manufacturing Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $3,760
OpenAlex ID: W4409524584