| Title |
XCNet: Enhancing Defect Detection in Sensor Boards Through Data Quality Analysis and Convolutional Neural Networks |
| Authors |
(Sachin Ranjan) ; (Hoon Kim) |
| DOI |
https://doi.org/10.5573/JSTS.2025.25.3.245 |
| Keywords |
deep learning; artificial intelligence; big data; quality control; digital; semiconductor; smart manufacturing; image recognition |
| Abstract |
Sensor boards are vital components in modern technologies, but ensuring their quality remains a significant challenge. Increasing demand has driven manufacturers to integrate more components onto single boards, complicating quality control processes. Defects in these boards pose risks of financial losses and safety hazards.
Traditional inspection methods, which rely on manual labor, are time-consuming, error-prone, and inefficient for handling complex products. Recent advancements in machine learning offer transformative solutions to these challenges. In this paper, we present XCNet, a convolutional neural network-based deep learning framework designed for automated defect detection in sensor boards. XCNet addresses the limitations of traditional methods by significantly enhancing inspection accuracy and efficiency while reducing human intervention. XCNet is tailored to handle highly imbalanced datasets caused by the rarity of defective products. Through comprehensive analyses, we investigate the impact of data quality on model performance, optimizing XCNet’s architecture and preprocessing techniques to achieve robust results. Extensive experiments on sensor board image data demonstrate XCNet’s remarkable accuracy of 99.54%, showcasing its potential as a reliable and scalable solution for automated quality control in manufacturing. |