Open AccessRoad Segmentation using Semantic egmentation Networks for ADAS
Author(s) -
Jong Bae Kim
Publication year - 2019
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.k1530.0981119
Subject(s) - segmentation , computer science , artificial intelligence , hough transform , computer vision , road surface , line (geometry) , boundary (topology) , class (philosophy) , image segmentation , pattern recognition (psychology) , image (mathematics) , engineering , mathematics , mathematical analysis , civil engineering , geometry
In this paper, we propose a method to automatically segment the road area from the input road images to support safe driving of autonomous vehicles. In the proposed method, the semantic segmentation network (SSN) is trained by using the deep learning method and the road area is segmented by utilizing the SSN. The SSN uses the weights initialized from the VGC-16 network to create the SegNet network. In order to fast the learning time and to obtain results, the class is simplified and learned so that it can be divided into two classes as the road area and the non-road area in the trained SegNet CNN network. In order to improve the accuracy of the road segmentation result, the boundary line of the road region with the straight-line component is detected through the Hough transform and the result is shown by dividing the accurate road region by combining with the segmentation result of the SSN. The proposed method can be applied to safe driving support by autonomously driving the autonomous vehicle by automatically classifying the road area during operation and applying it to the road area departure warning system