Cost‐Sensitive Siamese Network for PCB Defect Classification

Abstract

After the production of printed circuit boards (PCB), PCB manufacturers need to remove defected boards by conducting rigorous testing, while manual inspection is time-consuming and laborious. Many PCB factories employ automatic optical inspection (AOI), but this pixel-based comparison method has a high false alarm rate, thus requiring intensive human inspection to determine whether alarms raised from it resemble true or pseudo defects. In this paper, we propose a new cost-sensitive deep learning model: cost-sensitive siamese network (CSS-Net) based on siamese network, transfer learning and threshold moving methods to distinguish between true and pseudo PCB defects as a cost-sensitive classification problem. We use optimization algorithms such as NSGA-II to determine the optimal cost-sensitive threshold. Results show that our model improves true defects prediction accuracy to 97.60%, and it maintains relatively high pseudo defect prediction accuracy, 61.24% in real-production scenario. Furthermore, our model also outperforms its state-of-the-art competitor models in other comprehensive cost-sensitive metrics, with an average of 33.32% shorter training time.

Defect Detection in Printed Circuit Boards Using Semi-Supervised Learning

“…Previous studies have employed a transfer-learning approach to reduce the amount of training data [ 13 , 14 ]. Miao et al employed a cost-sensitive Siamese network based on transfer learning to differentiate defects in PCBs [ 14 ].…”

“…Previous studies have employed a transfer-learning approach to reduce the amount of training data [ 13 , 14 ]. Miao et al employed a cost-sensitive Siamese network based on transfer learning to differentiate defects in PCBs [ 14 ]. Imoto et al developed an automatic defect classification system that employed unreliably labeled data to train a convolutional neural network model to classify multiple defect types [ 10 ].…”

Single Object Tracking with Minimum False Positive using YOLOv4, VGG16, and Cosine Distance

“…Due to such problems, the siamese network method is the solution. The Siamese network is a comparison method that uses a neural network in it [9][10] [11][12] [13]. In this case, the siamese network extracts images with the same weight, then compares them with other images.…”