Comparison of unsupervised image anomaly detection models for sheet metal glue lines

Accurate anomaly detection and localization in sheet metal glue line applications are crucial for quality assurance in automotive manufacturing. Most current vision-based inspection systems that rely on geometric deviations from a predefined shape often suffer from high false-positive rates, leading to unnecessary interventions and operational inefficiencies. This research investigates the potential of unsupervised deep learning models to significantly reduce false positives in the analysis of sheet metal glue line images, even with limited datasets. We conducted a comparative evaluation of 17 unsupervised deep learning models covering different categories with 28 backbones on datasets of approximately 300 industrial glue line images per part from a Swedish vehicle manufacturer. A data synthesis method was applied to balance the glue line dataset, further enhancing the reliability of the models. To address the challenge of limited training data and improve model generalization, we incorporated data augmentation techniques and performed robustness experiments to ensure applicability to real-world industrial conditions. Our findings demonstrate that deep learning approaches can effectively detect and localize anomalies, significantly reducing false positives and gluing machine downtimes compared to the existing system. Moreover, we proposed a multi-criteria decision-making based approach for model selection, enabling decision-makers to achieve optimal trade-offs between accuracy and inference time, thus improving operational efficiency. These advancements highlight that even with limited training data, unsupervised deep learning models can enhance anomaly detection reliability, streamline the automotive production process, and reduce unnecessary resource expenditures.