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http://elartu.tntu.edu.ua/handle/lib/36932
| Teitl: | Classification of rolled metal defects using residual neural networks |
| Awduron: | Konovalenko, Ihor Maruschak, Pavlo Mosiy, Lyubomyr Duchon, Frantisek Kelemen, Michal |
| Affiliation: | Department of Industrial Automation, Ternopil National Ivan Puluj Technical University, Rus’ka str. 56, 46001 Ternopil, Ukraine Slovak University of Technology in Bratislava, Ilkovičova 3, SK-812 19, Bratislava; Slovak Republic Technical University of Kosice, Letna 9, 04200, Kosice, Slovak Republic |
| Bibliographic description (Ukraine): | Classification of rolled metal defects using residual neural networks / Ihor Konovalenko, Pavlo Maruschak, Lyubomyr Mosiy, Frantisek Duchon, Michal Kelemen // ICAAEIT 2021, 15-17 December 2021. — Tern. : TNTU, Zhytomyr «Publishing house „Book-Druk“» LLC, 2021. — P. 98–104. — (Electrical engineering and power electronics). |
| Bibliographic description (International): | Konovalenko I., Maruschak P., Mosiy L., Duchon F., Kelemen M. (2021) Classification of rolled metal defects using residual neural networks. ICAAEIT 2021 (Tern., 15-17 December 2021), pp. 98-104. |
| Is part of: | Proceedings of the International Conference „Advanced applied energy and information technologies 2021”, 2021 |
| Dyddiad Cyhoeddi: | 15-Dec-2021 |
| Date of entry: | 28-Dec-2021 |
| Cyhoeddwr: | TNTU, Zhytomyr «Publishing house „Book-Druk“» LLC |
| Place of the edition/event: | Ternopil |
| Temporal Coverage: | 15-17 December 2021 |
| Allweddeiriau: | metallurgy steel sheet surface defects visual inspection technology classification neural network |
| Number of pages: | 7 |
| Page range: | 98-104 |
| Start page: | 98 |
| End page: | 104 |
| Crynodeb: | The authors investigated deep residual neural networks, which are used to detect and classify defects found on the rolled metal surface. Based on the neural network with ResNet152 architecture, a classifier for recognizing defects of three classes was built. The proposed technique allows recognizing and classifying surface damage with high accuracy in real-time based on its image. The average binary accuracy of the classification made based on the test data is 97.3%. Neuron activation fields were studied in the convolutional layers of the model. The results obtained show that areas, which correspond to those with damage in the image, are activated. False-positive and false-negative cases of classifier application are investigated. Errors were found to occur most frequently in ambiguous situations when surface artefacts of different types are similar. |
| URI: | http://elartu.tntu.edu.ua/handle/lib/36932 |
| ISBN: | 978-617-8079-60-4 |
| Copyright owner: | © Ternopil Ivan Puluj National Technical University, Ukraine, 2021 |
| URL for reference material: | https://www.kaggle.com/c/severstal-steel-defect-detection |
| References (International): | 1.Chen, H., Hu, Q., Zhai, B. et al. (2020). A robust weakly supervised learning of deep Conv-Netsfor surface defect inspection. Neural Comput & Applic, 32, 11229–11244. doi:10.1007/s00521-020-04819-5 2.Dhua, S.K. (2019). Metallurgical analyses of surface defects in cold-rolled steel sheets. J Fail.Anal. and Preven, 19, 1023–1033. doi:10.1007/s11668-019-00690-2 3.Fang, X., Luo, Q., Zhou, B., Li, C., Tian, L. (2020). Research progress of automated visual surfacedefect detection for industrial metal planar materials. Sensors , 20, 5136, doi:10.3390/s20185136 4.François Chollet. (2017). Deep Learning with Python. Manning Publications. 5.GOST 21014-88. (1989). Rolled Products of Ferrous Metals. Surface Defects. Terms andDefinitions; Izd. Stand.: Moscow, USSR; p. 61. (In Russian) 6.He, K., Zhang, X., Ren, S., Sun, J. (2015). Deep Residual Learning for Image Recognition. arXiv,arXiv:1512.03385v1. 7.Kaggle Severstal: Steel Defect Detection. Can You Detect and Classify Defects in Steel? (2019).Kaggle. Retrieved from https://www.kaggle.com/c/severstal-steel-defect-detection. 8.Konovalenko, I., Maruschak, P., Brezinová, J., Viňáš, J., Brezina, J. (2020). Steel Surface DefectClassification Using Deep Residual Neural Network. Metals, 10, 846. 9.Kostenetskiy, P., Alkapov, R., Vetoshkin, N., Chulkevich, R., Napolskikh, I., Poponin, O. (2019).Real-time system for automatic cold strip surface defect detection. FME Trans. 47, 765–774. doi:10.5937/fmet1904765K. 10.Lee, S.Y., Tama, B.A., Moon, S.J., Lee, S. (2019). Steel Surface Defect Diagnostics Using DeepConvolutional Neural Network and Class Activation Map. Appl. Sci. 9, 5449, doi:10.3390/app9245449. 11.Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P. (2017). Focal Loss for Dense ObjectDetection. arXiv, arXiv:1708.02002v2. 12.Luo, Q., He, Y. (2016). A cost-effective and automatic surface defect inspection system for hot-rolled flat steel, Robotics and Computer-Integrated Manufacturing, 38, 16-30. 13.Takahashi, R., Matsubara, T., Uehara, K. (2018). RICAP: Random Image Cropping and PatchingData Augmentation for Deep CNNs. Conference on Machine Learning, Proceedings of The 10th Asian Conference. 14.Tao, X., Zhang, D., Ma, W., Liu, X., Xu, D. (2018). Automatic metallic surface defect detectionand recognition with convolutional neural networks. Appl. Sci. 8, 1575, doi:10.3390/app8091575. 15.Zhou, S., Chen, Y., Zhang, D., Xie, J., Zhou, Y. (2017). Classification of surface defects on steelsheet using convolutional neural networks. Mater Technology, 51(1):123. |
| Content type: | Conference Abstract |
| Ymddengys yng Nghasgliadau: | International conference „Advanced Applied Energy and Information Technologies 2021“, (ICAAEIT 2021) |
Ffeiliau yn yr Eitem Hon:
| Ffeil | Disgrifiad | Maint | Fformat | |
|---|---|---|---|---|
| ICAAEIT_2021_Konovalenko_I-Classification_of_rolled_98-104.pdf | 1,59 MB | Adobe PDF | Gweld/Agor | |
| ICAAEIT_2021_Konovalenko_I-Classification_of_rolled_98-104.djvu | 442,39 kB | DjVu | Gweld/Agor | |
| ICAAEIT_2021_Konovalenko_I-Classification_of_rolled_98-104__COVER.png | 447,65 kB | image/png | Gweld/Agor |
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