A Machine Vision-based Realtime Anomaly Detection Method for Industrial Products Using Deep Learning

Abstract

In the process of industrial production, anomaly detection is the key link to ensure the high quality of the product. This paper deeply studies the method of anomaly detection for industrial products based on deep learning. For the balanced image data set of industrial production products, this paper proposes a supervised anomaly detection model based on YOLOv3. This model constructs the ROI classifier to detect anomaly types. For the unbalanced image data set (only few anomaly images) of industrial production products, this paper proposes a semi-supervised anomaly detection model based on Fast-AnoGAN. This model is built from normal samples only. It uses the trained WGAN-GP model to generate images, and achieves anomaly detection by monitoring the anomaly score which is obtained by calculating the difference between the generated image and the test image. The two proposed anomaly detection models evaluated on both balanced and unbalanced data sets in the real industrial production scenarios. The performance evaluation results demonstrate that the two proposed anomaly detection models based on deep learning can well meet the dual requirements of real-time and accuracy for anomaly detection in the high-speed industrial production scenarios.