Pixel-level Anomaly Detection System for Casting Process Using an Unsupervised Deep Learning Model

This is the authors' abstract. We don't add key points for this paper.

Since collecting defect data in industrial environments requires considerable time and cost, unsupervised learning-based anomaly detection algorithms are being developed to solve this problem. However, research on anomaly detection in the casting process of producing impellers has focused on supervised learning approaches. Therefore, in this paper, we propose an unsupervised deep learning model for anomaly detection in the casting process. The autoencoder used in Efficient AD is limited in detecting fine-grained defect patterns in impeller data due to its upsampling reconstruction method. So, we change to an autoencoder that utilizes transposed convolution layers, a learnable upsampling method, to improve the detection of fine defects. In addition, we provide pixel-precise ground truth regions of impeller anomalies to evaluate pixel-level localization performance of various unsupervised anomaly detection algorithms in future research. Experimental results, through the impeller dataset, demonstrate the superiority in detection accuracy, inference efficiency, and particularly pixel-level localization.