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. Attached Files Name Description MIMEType Size Downloads Title Kangaroo vehicle collision detection using deep semantic segmentation convolutional neural network Author(s) Saleh, Khaled 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 Related Links Link Description Connect to published version Go to link with your DU access privileges   Connect to Elements publication management system Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Mon, 20 Mar 2017, 15:51:20 EST Mon, 20 Mar 2017, 15:53:16 EST Tue, 21 Mar 2017, 04:01:21 EST Tue, 21 Mar 2017, 05:31:52 EST Mon, 10 Apr 2017, 13:38:22 EST Mon, 10 Apr 2017, 13:40:40 EST Mon, 10 Apr 2017, 13:41:23 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 0 times in Scopus Search Google Scholar Access Statistics: 74 Abstract Views, 1 File Downloads  -  Detailed Statistics Created: Mon, 20 Mar 2017, 15:53:16 EST

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.