Image based Road Surface Classification Method using CNN
Dong-Geol Choi
Dong-Geol Choi, Department of Information and Communication Engineering, Hanbat National University, Daejeon, South Korea.
Manuscript received on 18 August 2019 | Revised Manuscript received on 28 August 2019 | Manuscript Published on 16 September 2019 | PP: 158-162 | Volume-8 Issue-2S6 July 2019 | Retrieval Number: B10300782S619/2019©BEIESP | DOI: 10.35940/ijrte.B1030.0782S619
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Road detection and road surface classification in autonomous driving are the most basic and important issues. In this paper, we propose a data augmentation method for road surface classification using image information. We design an optimal network that can classify the type of road surface from the input image information and propose a data increase technique that can efficiently judge by using limited data to improve learning performance. To verify the proposed methods, many running images were used on the Internet. Experimental vehicle was developed and applied to verify the developed networks and it shows that they operate accurately in real time.
Keywords: Road Surface Classification, CNN (Convolutional Neural Net), Data Augmentation, Resnet.
Scope of the Article: Classification