Automated defect inspection system for metal surfaces based on deep learning and data augmentation

Recent efforts to create a smart factory have inspired research that analyzes process data collected from Internet of Things (IOT) sensors, to predict product quality in real time. This requires an automatic defect inspection system that quantifies product quality data by detecting and classifying defects in real time. In this study, we propose a vision-based defect inspection system to inspect metal surface defects. In recent years, deep convolutional neural networks (DCNNs) have been used in many manufacturing industries and have demonstrated the excellent performance as a defect classification method. A sufficient amount of training data must be acquired, to ensure high performance using a DCNN. However, owing to the nature of the metal manufacturing industry, it is difficult to obtain enough data because some defects occur rarely. Owing to this imbalanced data problem, the generalization performance of the DCNN-based classification algorithm is lowered. In this study, we propose a new convolutional variational autoencoder (CVAE) and deep CNN-based defect classification algorithm to solve this problem. The CVAE-based data generation technology generates sufficient defect data to train the classification model. A conditional CVAE (CCVAE) is proposed to generate images for each defect type in a single CVAE model. We also propose a classifier based on a DCNN with high generalization performance using data generated from the CCVAE. In order to verify the performance of the proposed method, we performed experiments using defect images obtained from an actual metal production line. The results showed that the proposed method exhibited an excellent performance.