Machine learning versus human-developed algorithms in image analysis of microstructures

• Autorzy: Piwowarczyk Adam , Wojnar Leszek

Identyfikatory

DOI

10.2478/cqpi-2019-0056

• Języki publikacji: EN

Abstrakty

EN

Automatic image analysis is nowadays a standard method in quality control of metallic materials, especially in grain size, graphite shape and non-metallic content evaluation. Automatically prepared solutions, based on machine learning, constitute an effective and sufficiently precise tool for classification. Human-developed algorithms, on the other hand, require much more experience in preparation, but allow better control of factors affecting the final result. Both attempts were described and compared.

Słowa kluczowe

EN

image analysis; object detection; neural networks; machine learning

PL

analiza obrazu; detekcja obiektów; sieci neuronowe; uczenie maszynowe

• Wydawca: De Gruyter
• Czasopismo: Quality Production Improvement - QPI
• Rocznik: 2019
• Tom: Vol. 1, Iss. 1
• Strony: 412--416
• Opis fizyczny: Bibliogr. 8 poz., rys., tab.

Twórcy

autor

Piwowarczyk Adam

• Cracow University of Technology, Cracow, Poland

autor

Wojnar Leszek

• Cracow University of Technology, Cracow, Poland

Bibliografia

• 1.Brian L. DeCost, Elizabeth A. Holm, 2015. A computer vision approach for automated analysis and classification of microstructural image data, Computational Materials Science, 110, 126- 133, DOI: 10.1016/j.commatsci.2015.08.011
• 2.Chowdhury A., Kautz E., Yener B., Lewis D., 2016. Image driven machine learning methods for microstructure recognition, Computational Materials Science, 123, 176-187, DOI: 10.1016/j.commatsci.2016.05.034.
• 3.Geron A., 2018. Uczenie maszynowe z użyciem Scikit-Learn i TensorFlow, Helion SA, Gliwice Poland [in Polish].
• 4.Prasanna P., Dana K. J., Gucunski N., Basily B. B., Hung M. La, Lim R. S., Parvardeh H., 2014. Automated Crack Detection on Concrete Bridges, IEEE Transactions on Automation Science and Engineering, 13, 591-599, DOI: 10.1109/TASE.2014.2354314
• 5.Redmon J., Divvala S., Girshick R., Farhadi A., 2015. You Only Look Once. Unified, Real-Time Object Detection, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 779-788, DOI: 10.1109/CVPR.2016.91.
• 6.Shi Y., Cui L., Qi Z., Meng F., Chen Z., 2016. Automatic Road Crack Detection Using Random Structured Forests, IEEE Transactions on Intelligent Transportation Systems, 17, 3434- 3445, DOI: 10.1109/TITS.2016.2552248
• 7.Wojnar, L., 1999. Image analysis. Applications in materials engineering, CRC Press, Boca Raton, USA.
• 8.Zocca V., Spacagna G., Slater D., Roelants P., 2017. Deep learning. Uczenie głębokie z językiem Python. Sztuczna inteligencja i sieci neuronowe, Helion SA, Gliwice Poland [in Polish].

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).