Edge computing accelerated defect classification based on deep convolutional neural network with application in rolling image inspection

This paper develops a unified framework for training and deploying deep neural networks on the edge computing framework for image defect detection and classification. In the proposed framework, we combine the transfer learning and data augmentation with the improved accuracy given the small sample size. We further implement the edge computing framework to satisfy the real-time computational requirement. After the implement of the proposed model into a rolling manufacturing system, we conclude that deep learning approaches can perform around 30-40% better than some traditional machine learning algorithms such as random forest, decision tree, and SVM in terms of prediction accuracy. Furthermore, by deploying the CNNs in the edge computing framework, we can significantly reduce the computational time and satisfy the real-time computational requirement in the high-speed rolling and inspection system. Finally, the saliency map and embedding layer visualization techniques are used for a better understanding of proposed deep learning models.