Anomaly Detection for the Automated Visual Inspection of PET Preform Closures
Rippel O, Haumering P, Brauers J, Merhof D (2021)
Publication Type: Conference contribution
Publication year: 2021
Publisher: Institute of Electrical and Electronics Engineers Inc.
Book Volume: 2021-September
Conference Proceedings Title: IEEE International Conference on Emerging Technologies and Factory Automation, ETFA
Event location: Virtual, Vasteras, SWE
ISBN: 9781728129891
DOI: 10.1109/ETFA45728.2021.9613298
Abstract
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.
Involved external institutions
Germany (DE)How to cite
APA:
Rippel, O., Haumering, P., Brauers, J., & Merhof, D. (2021). Anomaly Detection for the Automated Visual Inspection of PET Preform Closures. In IEEE International Conference on Emerging Technologies and Factory Automation, ETFA. Virtual, Vasteras, SWE: Institute of Electrical and Electronics Engineers Inc..
MLA:
Rippel, Oliver, et al. "Anomaly Detection for the Automated Visual Inspection of PET Preform Closures." Proceedings of the 26th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2021, Virtual, Vasteras, SWE Institute of Electrical and Electronics Engineers Inc., 2021.
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