Imbalance Modelling for Defect Detection in Ceramic Substrate by Using Convolutional Neural Network

The complexity of defect detection in a ceramic substrate causes interclass and intraclass imbalance problems. Identifying flaws in ceramic substrates has traditionally relied on aberrant material occurrences and characteristic quantities. However, defect substrates in ceramic are typically small and have a wide variety of defect distributions, thereby making defect detection more challenging and difficult. Thus, we propose a method for defect detection based on unsupervised learning and deep learning. First, the proposed method conducts K-means clustering for grouping instances according to their inherent complex characteristics. Second, the distribution of rarely occurring instances is balanced by using augmentation filters. Finally, a convolutional neural network is trained by using the balanced dataset. The effectiveness of the proposed method was validated by comparing the results with those of other methods. Experimental results show that the proposed method outperforms other methods.

Automated summary

The complexity of defect detection in ceramic substrates leads to interclass and intraclass imbalance problems. Traditional methods rely on identifying flaws based on aberrant material occurrences and characteristic quantities. The proposed method utilizes unsupervised learning and deep learning to address the challenges of detecting defects in ceramic substrates, outperforming other methods according to experimental results.