2026-04-21T03:35:21Zhttps://ebiltegia.mondragon.edu/oai/request

oai:ebiltegia.mondragon.edu:20.500.11984/62912024-05-08T14:00:19Zcom_20.500.11984_1143col_20.500.11984_1148

00925njm 22002777a 4500 dc Intxausti Arbaiza, Eneko author Zugasti, Ekhi author Cernuda, Carlos author 2023 Defect detection plays a vital role in ensuring product quality and safety within industrial casting processes. In these dynamic environments, the occasional emergence of new defects in the production line poses a significant challenge for supervised methods. We present a defect detection framework to effectively detect novel defect patterns without prior exposure during training. Our method is based on contrastive learning applied to the Faster R-CNN model, enhanced with a contrastive head to obtain discriminative representations of different defects. By training on an diverse and comprehensive labeled dataset, our method achieves comparable performance to the supervised baseline model, showcasing commendable defect detection capabilities. To evaluate the robustness of our approach, we authentically replicate a real-world use case by deliberately excluding several defect types from the training data. Remarkably, in this new context, our proposed method significantly improves detection performance of the baseline model, particularly in situations with very limited training data, showcasing a remarkable 34.7% enhancement. Our research highlights the potential of the proposed method in real-world environments where the number of available images may be limited or inexistent. By providing valuable insights into defect detection in challenging scenarios, our framework could contribute to ensuring efficient and reliable product quality and safety in industrial manufacturing processes. 978-3-031-49018-7 1611-3349 https://katalogoa.mondragon.edu/janium-bin/janium_login_opac.pl?find&ficha_no=173969 https://hdl.handle.net/20.500.11984/6291 Defect detection contrastive learning casting optical quality control deep learning Towards robust defect detection in casting using contrastive learning