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A convolutional neural network based deep learning technique for identifying road attributes
conference contribution
posted on 2019-10-29, 00:00 authored by Muhammad Zohaib JanMuhammad Zohaib Jan, Brijesh Verma, J Affum, S Atabak, L MoirAutomatic assessment of road safety and conditions is essential for improving road infrastructure and reducing fatalities on the roads. The current manual systems used for road safety not only in Australia but around the world are inefficient and prone to many errors. The major challenges are to accurately detect, segment and classify all road objects and also calculate the distance between the objects. Deep learning with a recent breakthrough has the ability to address such major challenges. In this paper, we propose a novel deep learning approach that can analyze video data and assess road safety and conditions. The specific aims are to develop a novel convolutional neural network based segmentation and classification technique for automatically identifying road attributes for Australian Road Assessment Program (AusRAP) and a novel proximity measurement technique for distance measurement between AusRAP attributes. The proposed approach has been evaluated on the roadside video data collected by the Queensland department of transport and main roads and the results are presented.
History
Start Page
271End Page
276Number of Pages
6Start Date
2018-11-19Finish Date
2018-11-21ISSN
2151-2191ISBN-13
9781728101262Location
Auckland, New ZealandPublisher
IEEEPlace of Publication
Piscataway, NJPublisher DOI
Full Text URL
Peer Reviewed
- Yes
Open Access
- No
External Author Affiliations
Australian Road Research Board; Dept of Transport and Main Roads, BrisbaneAuthor Research Institute
- Centre for Intelligent Systems
Era Eligible
- Yes