Classification of Types of Automobile Fractures Using Convolutional Neural Networks

  • Nikhil SonavaneEmail author
  • Ambarish Moharil
  • Fagun Shadi
  • Mrunal Malekar
  • Sourabh Naik
  • Shashank Prasad
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1101)


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.


Convolution Deep learning Max pooling 


  1. 1.
    Jia, Xin. 2017. Image recognition method based on deep learning. In 29th Chinese Control and Decision Conference (CCDC).Google Scholar
  2. 2.
    Guo, Tianmei, Jiwen Dong, Henjian Li, Yunxing Gao. 2017. Simple convolutional neural network on image classification. In 2017 IEEE 2nd International Conference on Big Data Analysis.Google Scholar
  3. 3.
  4. 4.
    Yim, Junho, Jeongwoo Ju, Heechul Jung, and Junmo Kim. 2015. Image classification using convolutional neural networks with multi-stage feature. In Robot Intelligence Technology and Applications 3. Advances in Intelligent Systems and Computing, ed. J.-H. Kim et al., vol. 345, 587. Springer International Publishing Switzerland.Google Scholar
  5. 5.
    Tajbakhsh, N., et al. 2016. Convolutional neural networks for medical image analysis: Full training or fine tuning? IEEE Transactions on Medical Imaging 35 (5).Google Scholar
  6. 6.
    Krizhevsky, A., I. Sutskever, and G.E. Hinton. 2012. ImageNet classification with deep convolutional neural networks. In Proceedings of Advances in neural information processing systems.Google Scholar
  7. 7.
    He, K., X. Zhang, S. Ren, and J. Sun. 2016. Deep residual learning for image recognition. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, June.Google Scholar
  8. 8.
    Boureau, Y.L., F. Bach, Y. LeCun, and J. Ponce. 2010. Learning midlevel features for recognition. In CVPR. Google Scholar
  9. 9.
  10. 10.
    Jmour, Nadia, Sehla Zayen, and Afef Abdelkrim. 2018. Convolutional neural networks for image classification. 978-1-5386-4449-2/18/$31.00 ©2018 IEEE.Google Scholar
  11. 11.
    Wang, Tao, David J. Wu, Adam Coates, and Andrew Y. Ng. End-to-end text recognition with convolutional neural networks.Google Scholar
  12. 12.
    Vo, An Tien, Hai Son Tran, Thai Hoang Le. 2017. Advertisement image classification using convolutional neural network. In 2017 9th International Conference on Knowledge and Systems Engineering (KSE).Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Nikhil Sonavane
    • 1
    Email author
  • Ambarish Moharil
    • 1
  • Fagun Shadi
    • 1
  • Mrunal Malekar
    • 1
  • Sourabh Naik
    • 1
  • Shashank Prasad
    • 1
  1. 1.Department of Electronics and Telecommunication EngineeringVishwakarma Institute of TechnologyPuneIndia

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