Road-Type Classification through Deep Learning Networks Fine-Tuning

Yaser Saleh () and Nesreen Otoum ()
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Yaser Saleh: Department of Software Engineering, University of Petra, Amman, Jordan
Nesreen Otoum: Department of Software Engineering, University of Petra, Amman, Jordan

Journal of Information & Knowledge Management (JIKM), 2020, vol. 19, issue 01, 1-12

Abstract: Road-type classification is increasingly becoming important to be embedded in interactive maps to provide additional useful information for users. The ubiquity of smartphones supported with high definition cameras offers a rich source of information that can be utilised by machine learning techniques. In this paper, we propose a novel Convolutional Neural Network (CNN)-based approach to classify road types using a collection of publicly available images. To overcome the challenge of having huge dataset to train and test CNNs, our approach employs fine-tuning. We conducted an experiment where the VGG-16, VGG-S and GoogLeNet networks were constructed and fine-tuned with the dataset gathered. Our approach achieved an accuracy of 99% in VGG-16 and 100% in VGG-S, while using the GoogLeNet model produced results up to 98%.

Keywords: Road-type classification; deep learning; fine-tuning; convolutional neural network (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:wsi:jikmxx:v:19:y:2020:i:01:n:s0219649220400201

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DOI: 10.1142/S0219649220400201

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