[KSCI] Korea Science Citation Index Service

http://dx.doi.org/10.33851/JMIS.2020.7.1.41

Drivable Area Detection with Region-based CNN Models to Support Autonomous Driving

Jeon, Hyojin (Dep.of Com. Science & Info. Eng., Korea National University of Transportation)
Cho, Soosun (Dep.of Com. Science & Info. Eng., Korea National University of Transportation)

Publication Information

Journal of Multimedia Information System / v.7, no.1, 2020 , pp. 41-44 More about this Journal

Abstract

In autonomous driving, object recognition based on machine learning is one of the core software technologies. In particular, the object recognition using deep learning becomes an essential element for autonomous driving software to operate. In this paper, we introduce a drivable area detection method based on Region-based CNN model to support autonomous driving. To effectively detect the drivable area, we used the BDD dataset for model training and demonstrated its effectiveness. As a result, our R-CNN model using BDD datasets showed interesting results in training and testing for detection of drivable areas.

Keywords

Detection of drivable areas; Region-based CNN; BDD dataset; Autonomous driving software;

Citations & Related Records

Reference

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