TY - GEN
T1 - Road detection from aerial imagery
AU - Lin, Yucong
AU - Saripalli, Srikanth
PY - 2012/1/1
Y1 - 2012/1/1
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
T3 - Proceedings - IEEE International Conference on Robotics and Automation
SP - 3588
EP - 3593
BT - 2012 IEEE International Conference on Robotics and Automation, ICRA 2012
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2012 IEEE International Conference on Robotics and Automation, ICRA 2012
Y2 - 14 May 2012 through 18 May 2012
ER -