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Kangaroo vehicle collision detection using deep semantic segmentation convolutional neural network

Saleh, Khaled, Hossny, Mohammed and Nahavandi, Saeid 2016, Kangaroo vehicle collision detection using deep semantic segmentation convolutional neural network, in DICTA 2016 : Proceedings of the IEEE International Conference on Digital Image Computing: Techniques and Applications, IEEE, Piscataway, N.J., doi: 10.1109/DICTA.2016.7797057.

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Title Kangaroo vehicle collision detection using deep semantic segmentation convolutional neural network
Author(s) Saleh, Khaled
Hossny, MohammedORCID iD for Hossny, Mohammed orcid.org/0000-0002-1593-6296
Nahavandi, Saeid
Conference name Digital Image Computing: Techniques and Applications. International Conference (2016 : Gold Coast, Qld.)
Conference location Gold Coast, Qld.
Conference dates 30 Nov - 2 Dec 2016
Title of proceedings DICTA 2016 : Proceedings of the IEEE International Conference on Digital Image Computing: Techniques and Applications
Publication date 2016
Publisher IEEE
Place of publication Piscataway, N.J.
Keyword(s) Science & Technology
Technology
Computer Science, Theory & Methods
Engineering, Electrical & Electronic
Imaging Science & Photographic Technology
Computer Science
Engineering
Kangaroo detection
vehicle collision
deep learning
ConvNet and semantic segmentation
Summary Kangaroo vehicle collisions are a serious problem threatening the safety of the drivers on Australian roads. It is estimated, according to a recent report by Australian Associated Motor Insurers, that there are around 20,000 kangaroo vehicle collisions during year 2015 in Australia. As a result, more than AU $75 million in insurance claims, and a number of animal and human severe injuries and fatalities have been reported. Despite how catastrophic these numbers are, yet a little research has been done in order to avoid or minimise the number of kangaroo vehicle collisions. In this work, we are focusing on the problem of recognising and detecting kangaroos in dynamic environments using a deep semantic segmentation convolutional neural network model. Our model is trained on a synthetic labelled depth images obtained using a simulated range sensor. Our approach records average recall value of over 93% in semantically segmenting any number of kangaroos in the generated testing dataset.
ISBN 9781509028979
Language eng
DOI 10.1109/DICTA.2016.7797057
Field of Research 099999 Engineering not elsewhere classified
Socio Economic Objective 0 Not Applicable
HERDC Research category E1 Full written paper - refereed
ERA Research output type E Conference publication
Copyright notice ©2016, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30092160

Document type: Conference Paper
Collection: Centre for Intelligent Systems Research
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