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

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.<br/>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.