TY - GEN
T1 - Unmanned ground operations using semantic image segmentation through a Bayesian network
AU - Coombes, Matthew
AU - Eaton, Will
AU - Chen, Wen Hua
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/6/30
Y1 - 2016/6/30
N2 - This paper discusses the machine vision element of a system designed to allow automated taxiing for Unmanned Aerial System (UAS) around civil aerodromes. The purpose of the computer vision system is to provide direct sensor data which can be used to validate vehicle position, in addition to detect potential collision risks. This is achieved through the use of a singular monocular sensor. Untrained clustering is used to segment the visual feed before descriptors of each cluster (primarily colour and texture) are then used to estimate the class. As the competency of each individual estimate can vary based on multiple factors (number of pixels, lighting conditions and even surface type) a Bayesian network is used to perform probabilistic data fusion, in order to improve the classification results. This result is shown to perform accurate image segmentation in real-world conditions, providing information viable for map matching.
AB - This paper discusses the machine vision element of a system designed to allow automated taxiing for Unmanned Aerial System (UAS) around civil aerodromes. The purpose of the computer vision system is to provide direct sensor data which can be used to validate vehicle position, in addition to detect potential collision risks. This is achieved through the use of a singular monocular sensor. Untrained clustering is used to segment the visual feed before descriptors of each cluster (primarily colour and texture) are then used to estimate the class. As the competency of each individual estimate can vary based on multiple factors (number of pixels, lighting conditions and even surface type) a Bayesian network is used to perform probabilistic data fusion, in order to improve the classification results. This result is shown to perform accurate image segmentation in real-world conditions, providing information viable for map matching.
KW - Bayesian Network
KW - Domain Knowledge
KW - Semantic Image Segmentation
KW - Unmanned Ground Operations
UR - http://www.scopus.com/inward/record.url?scp=84979780282&partnerID=8YFLogxK
U2 - 10.1109/ICUAS.2016.7502572
DO - 10.1109/ICUAS.2016.7502572
M3 - Conference article published in proceeding or book
AN - SCOPUS:84979780282
T3 - 2016 International Conference on Unmanned Aircraft Systems, ICUAS 2016
SP - 868
EP - 877
BT - 2016 International Conference on Unmanned Aircraft Systems, ICUAS 2016
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2016 International Conference on Unmanned Aircraft Systems, ICUAS 2016
Y2 - 7 June 2016 through 10 June 2016
ER -