PCB Defect Detection using Deep Learning and Synthetic Data Generation with ControlNet
- 点击次数：
- DOI码：10.15680/IJIRCCE.2025.1301146
- 发表刊物：IJIRCCE
- 关键字：PCB Defect Detection; Synthetic Data Generation; Swin Transformer; ControlNet
- 摘要：This defect detection in printed circuit boards (PCBs) is crucial to ensure reliability and functionality of
the equipment used in all Industries. Based on the analysis, this paper develops a new PCB defect detector composed of
Swin Transformer and synthetic data generation based on ControlNet. Lack of availability of data and expensive
labeling process makes the traditional method of defect identification unproductive. To overcome these challenges, this
study uses ControlNet to create relevant synthetic defect samples that enrich the dataset and enhance generalization.
Experimental findings show high increase in performance from 87.5% to 93.4% in accuracy; 85.2% to 91.8% in
precision and 83.6% to 91.2% in F1-score by integrating synthetic data. The proposed approach had better performance
than baseline models, including CNN and YOLOv5, especially in detecting complex and rare defects. The findings of
this research also serve the purpose of enhancing the reliability and efficiency of PCB inspection to reveal the
effectiveness of using more sophisticated methods of data augmentation and transformer-based models in industrial
problems.
- 第一作者：文学志
- 论文类型：期刊论文
- 通讯作者：Zamadder Md Iqbal Hossain
- 卷号：13
- 期号：1
- 是否译文：否
- 发表时间：2025-01-19
- 文学志_PCB Defect Detection using Deep Learning and Synthetic Data Generation with ControlNet.pdf 下载[] 次
