Future of Information and Communication Conference (FICC) 2024
4-5 April 2024
Publication Links
IJACSA
Special Issues
Future of Information and Communication Conference (FICC)
Computing Conference
Intelligent Systems Conference (IntelliSys)
Future Technologies Conference (FTC)
International Journal of Advanced Computer Science and Applications(IJACSA), Volume 11 Issue 6, 2020.
Abstract: 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.
Elizabeth Endri, Alaa Sheta and Hamza Turabieh, “Road Damage Detection Utilizing Convolution Neural Network and Principal Component Analysis” International Journal of Advanced Computer Science and Applications(IJACSA), 11(6), 2020. http://dx.doi.org/10.14569/IJACSA.2020.0110682
@article{Endri2020,
title = {Road Damage Detection Utilizing Convolution Neural Network and Principal Component Analysis},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2020.0110682},
url = {http://dx.doi.org/10.14569/IJACSA.2020.0110682},
year = {2020},
publisher = {The Science and Information Organization},
volume = {11},
number = {6},
author = {Elizabeth Endri and Alaa Sheta and Hamza Turabieh}
}
Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.