Abstract
Image classification has recently been in serious attention of various researchers as one of the most upcoming fields. For this, various algorithms have been developed and used by researchers. In recent years, convolutional neural networks have gained huge popularity among masses for image classification and feature extraction. In this project, we have used convolutional neural networks for the classification of automobile fractures using their micrographs available on the Internet into their three known types—ductile, fatigue, and brittle. We have used a specific algorithm to extract the best epoch model from the whole model due to loss in the accuracy we encountered.
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Sonavane, N., Moharil, A., Shadi, F., Malekar, M., Naik, S., Prasad, S. (2020). Classification of Types of Automobile Fractures Using Convolutional Neural Networks. In: Swain, D., Pattnaik, P., Gupta, P. (eds) Machine Learning and Information Processing. Advances in Intelligent Systems and Computing, vol 1101. Springer, Singapore. https://doi.org/10.1007/978-981-15-1884-3_13
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DOI: https://doi.org/10.1007/978-981-15-1884-3_13
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