A novel FuseDecode Autoencoder for industrial visual inspection: Incremental anomaly detection improvement with gradual transition from unsupervised to mixed-supervision learning with reduced human effort
提出FuseDecode自编码器，通过从无监督到混合监督的增量学习，逐步提升工业异常检测性能，减少人工标注成本，在真实工业数据和MVTec AD数据集上验证了有效性。
The industrial implementation of automated visual inspection leveraging deep learning is limited due to the labor-intensive labeling of datasets and the lack of datasets containing images of defects, which is especially the case in high-volume manufacturing with zero defect constraints. In this study, we present the FuseDecode Autoencoder (FuseDecode AE), a novel reconstruction-based anomaly detection model featuring incremental learning. Initially, the FuseDecode AE operates in an unsupervised manner on noisy data containing predominantly normal images and a small number of anomalous images. The predictions generated assist experts in distinguishing between normal and anomalous samples. Later, it adapts to weakly labeled datasets by retraining in a semi-supervised manner on normal data augmented with synthetic anomalies. As more real anomalous samples become available, the model further refines its capabilities through mixed-supervision learning on both normal and anomalous samples. Evaluation on a real industrial dataset of coating defects shows the effectiveness of the incremental learning approach. Furthermore, validation on the publicly accessible MVTec AD dataset demonstrates the FuseDecode AE's superiority over other state-of-the-art reconstruction-based models. These findings underscore its generalizability and effectiveness in automated visual inspection tasks, particularly in industrial settings. • A novel FuseDecode Autoencoder facilitates incremental learning for anomaly detection. • Unsupervised learning on noisy datasets supports efficient dataset labelling. • Semi-supervised learning with synthetic anomalies ensures reliable anomaly detection. • Mixed supervision learning on real anomalous samples enhances detection performance. • Method validation was done on a real industrial and on the public MVTec AD dataset.