International Journal of Nonlinear Analysis and Applications

Transfer learning based deep convolutional neural network model for pavement crack detection from images

Document Type : Research Paper

Authors

1 Department of Electronics and Communication Engineering, Veltech Rangarajan Dr Sagunthala R &D Institute of Science and Technology, Chennai. Tamil Nadu-600062

2 Department of Computer Science and Engineering, Amrita School of Engineering, Bengaluru campus, Amrita Vishwa Vidyapeetham, India

Abstract

The road is a path that supports to connect different places. It plays a crucial role in our day-today life. Improper maintenance, overloading, climate conditions, and some other elements create distress on the roads. The common distresses are Potholes, cracking, and rutting. Manually detecting the distresses means human inspection is a messy and long time-consuming process. In recent past accidents on road is on the increase due to improper maintenance of road. Efficient methods of detecting pavement damages using image processing, machine learning and deep learning techniques have been a trending research topic. Image processing algorithms mainly include edge detection, region growing methods, and threshold segmentation operations for processing the pavement images and extracting crack information from the images. Machine learning methods of pavement crack detection adapts neural networks, supervised and unsupervised learning algorithms with pavement crack image as input. With Deep learning techniques, it has been possible to detect pavement cracks with greater accuracy. In this paper, we review the deep learning methods of pavement crack detection and propose a novel method to detect pavement cracks using Deep Learning with transfer learning. We also analyzed the performance of the proposed model for different network architectures namely, Google net, Alexnet and Resent and inferred that Google net gives better performance in detecting pavement cracks.

Keywords

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International Journal of Nonlinear Analysis and Applications
Volume 13, Issue 1
March 2022
Pages 1209-1223
  • Receive Date: 12 June 2021
  • Revise Date: 30 June 2021
  • Accept Date: 19 October 2021