Road Damage Detection Utilizing Convolution Neural Network and Principal Component Analysis

Roads should always be in a reliable con-dition and maintained regularly. One of the problems that should be maintained well is the pavement cracks problem. This a challenging problem that faces road engineers, since maintaining roads in a stable condition is needed for both drivers and pedestrians. Many meth-ods have been proposed to handle this problem to save time and cost. In this paper, we proposed a two-stage method to detect pavement cracks based on Principal Component Analysis (PCA) and Convolutional Neural Network (CNN) to solve this classification problem. We employed a Principal Component Analysis (PCA) method to extract the most significant features with a di˙erent number of PCA components. The proposed approach was trained using a Mendeley Asphalt Crack dataset, which contains 400 images of road cracks with a 480×480 resolution. The obtained results show how PCA helped in speeding up the learning process of CNN.

@article{Endri2020,
  title     = {Road Damage Detection Utilizing Convolution Neural Network and Principal Component Analysis},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {11},
  number    = {6},
  year      = {2020},
  publisher = {The Science and Information Organization},
  author    = {Elizabeth Endri and Alaa Sheta and Hamza Turabieh},
  doi       = {10.14569/IJACSA.2020.0110682},
  url       = {https://doi.org/10.14569/IJACSA.2020.0110682}
}