Zero-shot learning and classification of steel surface defects

Amr M. Nagy; László Czúni

doi:10.1117/12.2623570

4 March 2022 Zero-shot learning and classification of steel surface defects

Amr M. Nagy, László Czúni

Proceedings Volume 12084, Fourteenth International Conference on Machine Vision (ICMV 2021); 120841C (2022) https://doi.org/10.1117/12.2623570
Event: Fourteenth International Conference on Machine Vision (ICMV 2021), 2021, Rome, Italy

Abstract

Due to the manufacturing process and environmental effects steel surfaces can have a variety of defects. The nonuniform surface brightness and the variety of shapes of defects make their detection challenging. In our paper we propose neural networks for the recognition of new defect classes and also for the classification of known types. For the former a zero-shot approach, based on a siamese network, is used learning features to classify unseen classes without a single training example. Additionally, we can utilize one branch (one structural part) of the same network for the classifications of previously trained defects. For performance evaluations, experiments were carried out on two benchmark data-sets: the Northeastern University and the Xsteel surface defect data-sets. Results show that our method outperforms the state-of-the-art solutions on the NEU data-set for zero-shot learning and for classification with accuracy 85.80% and 100% respectively. In case of the Xsteel data-set, we reached 98% for classification (which is the top known performance).

Citation Download Citation

Amr M. Nagy and László Czúni "Zero-shot learning and classification of steel surface defects", Proc. SPIE 12084, Fourteenth International Conference on Machine Vision (ICMV 2021), 120841C (4 March 2022); https://doi.org/10.1117/12.2623570

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KEYWORDS

Neural networks

Defect detection

Iron

Image classification

Oxides

Databases

Feature extraction

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