Abstract
We present a fast, robust road detection algorithm for aerial images taken from an Unmanned Aerial Vehicle. A histogram-based adaptive threshold algorithm is used to detect possible road regions in an image. A probabilistic hough transform based line segment detection combined with a clustering method is implemented to further extract the road. The proposed algorithm has been extensively tested on desert and urban images obtained using an Unmanned Aerial Vehicle. Our results indicate that we are able to successfully and accurately detect roads in 97% of the images. We experimentally validated our algorithm on over ten thousand (10,000) aerial images obtained using our UAV. These images consist of intersecting roads, bifurcating roads and roundabouts in various conditions with significant changes in lighting and intensity. Our algorithm is able to successfully detect single roads effectively in almost all the images. It is also able to detect at least one road in over 95% of the images containing bifurcating or intersecting roads.
| Original language | English (US) |
|---|---|
| Title of host publication | Proceedings - IEEE International Conference on Robotics and Automation |
| Pages | 3588-3593 |
| Number of pages | 6 |
| DOIs | |
| State | Published - 2012 |
Fingerprint
ASJC Scopus subject areas
- Software
- Artificial Intelligence
- Control and Systems Engineering
- Electrical and Electronic Engineering
Cite this
Road detection from aerial imagery. / Lin, Yucong; Saripalli, Srikanth.
Proceedings - IEEE International Conference on Robotics and Automation. 2012. p. 3588-3593 6225112.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Road detection from aerial imagery
AU - Lin, Yucong
AU - Saripalli, Srikanth
PY - 2012
Y1 - 2012
N2 - We present a fast, robust road detection algorithm for aerial images taken from an Unmanned Aerial Vehicle. A histogram-based adaptive threshold algorithm is used to detect possible road regions in an image. A probabilistic hough transform based line segment detection combined with a clustering method is implemented to further extract the road. The proposed algorithm has been extensively tested on desert and urban images obtained using an Unmanned Aerial Vehicle. Our results indicate that we are able to successfully and accurately detect roads in 97% of the images. We experimentally validated our algorithm on over ten thousand (10,000) aerial images obtained using our UAV. These images consist of intersecting roads, bifurcating roads and roundabouts in various conditions with significant changes in lighting and intensity. Our algorithm is able to successfully detect single roads effectively in almost all the images. It is also able to detect at least one road in over 95% of the images containing bifurcating or intersecting roads.
AB - We present a fast, robust road detection algorithm for aerial images taken from an Unmanned Aerial Vehicle. A histogram-based adaptive threshold algorithm is used to detect possible road regions in an image. A probabilistic hough transform based line segment detection combined with a clustering method is implemented to further extract the road. The proposed algorithm has been extensively tested on desert and urban images obtained using an Unmanned Aerial Vehicle. Our results indicate that we are able to successfully and accurately detect roads in 97% of the images. We experimentally validated our algorithm on over ten thousand (10,000) aerial images obtained using our UAV. These images consist of intersecting roads, bifurcating roads and roundabouts in various conditions with significant changes in lighting and intensity. Our algorithm is able to successfully detect single roads effectively in almost all the images. It is also able to detect at least one road in over 95% of the images containing bifurcating or intersecting roads.
UR - http://www.scopus.com/inward/record.url?scp=84864435928&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84864435928&partnerID=8YFLogxK
U2 - 10.1109/ICRA.2012.6225112
DO - 10.1109/ICRA.2012.6225112
M3 - Conference contribution
AN - SCOPUS:84864435928
SN - 9781467314039
SP - 3588
EP - 3593
BT - Proceedings - IEEE International Conference on Robotics and Automation
ER -