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Comparative Analysis of Deep Neural Networks for Crack Image Classification

  • Sheerin Sitara Noor MohamedEmail author
  • Kavitha Srinivasan
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 38)

Abstract

Deep Learning (DL) is widely used in different types of classification problems in real time. DL models can be constructed in two ways namely, Convolutional Neural Network (CNN) and using pre-trained models such as VGG16, VGG19 and Inception ResNet V2. In this paper, an automatic crack image classification approach is proposed and implemented using CNN and pre–trained models. For validation, three types of datasets having both enhanced and without enhanced crack images are used and the result of classification are analysed using appropriate quantitative metrics such as accuracy, precision and recall, from the results, it has been inferred that the proposed CNN, derived the highest accuracy of 99% for dataset 1 whereas Inception ResNet V2 model, derived the highest accuracy of 87% and 94% for dataset 2 and dataset 3 respectively.

Keywords

Crack classification Convolutional Neural Network Pre-trained models Transfer learning approach Deep Learning 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Sheerin Sitara Noor Mohamed
    • 1
    Email author
  • Kavitha Srinivasan
    • 1
  1. 1.Department of Computer Science and EngineeringSSN College of EngineeringChennaiIndia

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