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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
  • 11 Downloads
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1101)

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.

Keywords

Convolution Deep learning Max pooling 

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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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