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- W608560975 abstract "This paper describes work underway at the University of Tasmania to develop low-cost image-based neural network systems to assess road infrastructure condition. Images are pre-processed using image analysis methods to produce pixel arrays that are then passed to feed-forward back-propagation neural networks, together with ancillary information such as spectral characteristics. The networks are trained to identify road asset features, such as line markings and road side vegetation, and to assess their condition. This can be done either in real time, using live camera feed, or from videos recorded by road patrol vehicles. (a) For the covering entry of this conference, please see ITRD abstract no. E210218." @default.
- W608560975 created "2016-06-24" @default.
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- W608560975 date "2003-01-01" @default.
- W608560975 modified "2023-09-27" @default.
- W608560975 title "ROAD CONDITION ASSESSMENT USING FEATURE RECOGNITION BY NEURAL NETWORKS" @default.
- W608560975 hasPublicationYear "2003" @default.
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