Anomaly Detection for the Automated Visual Inspection of PET Preform Closures

Recent advances in supervised Machine Learning have enabled the automated visual inspection of increasingly complex products. The economic feasibility of developed methods is, however, limited by their requirement for large amounts of labeled training data. As an alternative, Anomaly Detection (AD) methods have been proposed, which do not rely on large-scale datasets and often require defect-free images only. In our work, we benchmark and adapt proposed state-of-the-art AD as well as Anomaly Segmentation (AS) methods to the Polyethylene Terephthalate (PET) preform closure inspection task in a high-throughput setting. We furthermore compare AD/AS methods with fully supervised baselines and show that, in the low-data regime, comparable AS performance can be achieved. Moreover, fully supervised baselines are outperformed with respect to AD by our adapted methods. We also conduct ablation studies to quantify the effects of proposed modifications, and show that they reduce memory footprint by a factor of 3 while maintaining AD/AS performance. Together, this demonstrates the suitability of AD/AS methods for the automated visual inspection of complex objects such as transparent PET preform closures in high-throughput settings.