Volume XLII-4/W18
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4/W18, 371–377, 2019
https://doi.org/10.5194/isprs-archives-XLII-4-W18-371-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4/W18, 371–377, 2019
https://doi.org/10.5194/isprs-archives-XLII-4-W18-371-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

  18 Oct 2019

18 Oct 2019

ROAD CRACK DETECTION USING GAUSSIAN/PREWITT FILTER

S. A. Fakhri1, S. A. Fakhri2, and M. Saadatseresht2 S. A. Fakhri et al.
  • 1Department of Photogrammetry and Remote Sensing, Faculty of Geodesy and Geomatics Engineering, K. N. Toosi University of Technology, Tehran, Iran
  • 2School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, Iran

Keywords: Road Crack, road quality, random forest, close range, image processing, Noise reduction

Abstract. Crack is one of the main problems of roads that can reduce the quality of the road or put it in danger in terms of safety. These cracks are needed to be identified first in order to be investigated and followed up. So detecting cracks is one of the most important parts of the road maintenance procedures, which has been considered in recent years. On the other hand, traditional manual methods of crack detection are very time-consuming and dangerous. It is timely because human resources must have thorough and accurate road visits and examine their qualitative status. For this reason, the automatic methods are utilized instead of these methods to increase the speed and reliability of the crack analysis in intelligent transport systems. A simple method is used in this research to detect the crack. Based on the proposed method, a softening filter is applied first on the image to reduce the noise, and then an edge detection filter is applied to the image. Generally, the noise still exists in the image after applying these filters. A window is used here that scans all the image and calculates the average standard deviation for all the pixels in each window, and removes the noise by considering a range. Then the process of removing noise is done with more stringency by reducing the search window in each iteration. Finally, this method was compared with one of the most prominent and modern methods of detecting cracks using a random forest method and the results indicated that despite the simplicity and the speed of the existing method in this study, it has an acceptable performance compared to the manual and random forest methods.