Version 1
: Received: 29 June 2019 / Approved: 1 July 2019 / Online: 1 July 2019 (11:33:38 CEST)
How to cite:
Dadrasjavan, F.; Zarrinpanjeh, N.; Ameri, A. Automatic Crack Detection of Road Pavement Based on Aerial UAV Imagery. Preprints2019, 2019070009. https://doi.org/10.20944/preprints201907.0009.v1
Dadrasjavan, F.; Zarrinpanjeh, N.; Ameri, A. Automatic Crack Detection of Road Pavement Based on Aerial UAV Imagery. Preprints 2019, 2019070009. https://doi.org/10.20944/preprints201907.0009.v1
Dadrasjavan, F.; Zarrinpanjeh, N.; Ameri, A. Automatic Crack Detection of Road Pavement Based on Aerial UAV Imagery. Preprints2019, 2019070009. https://doi.org/10.20944/preprints201907.0009.v1
APA Style
Dadrasjavan, F., Zarrinpanjeh, N., & Ameri, A. (2019). Automatic Crack Detection of Road Pavement Based on Aerial UAV Imagery. Preprints. https://doi.org/10.20944/preprints201907.0009.v1
Chicago/Turabian Style
Dadrasjavan, F., Nima Zarrinpanjeh and Azam Ameri. 2019 "Automatic Crack Detection of Road Pavement Based on Aerial UAV Imagery" Preprints. https://doi.org/10.20944/preprints201907.0009.v1
Abstract
Road surface monitoring more specifically crack detection on the surface of the road pavement is a complicated task which is found vital due to critical nature of roads as elements of transportation infrastructure. Cracks on the road pavement is detectable using remotely sensed imagery or car mounted platforms. UAV’s are also considered as useful tools for acquiring reliable information about the pavement of the road. In This paper, an automatic method for crack detection on the road pavement is proposed using acquired videos from UAV platform. Selecting key frames and generating Ortho-image, violating non road regions in the scene are removed. Then through an edge based approach hypothesis crack elements are extracted. Afterwards, through SVM based classification true cracks are detected. Developing the proposed method, the generated results show 75% accuracy in crack detection while less than 10% of cracks are omitted.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
