Intersections and crosswalk detection using deep learning and image processing techniques

Vedat Tümen and Burhan Ergen

Physica A: Statistical Mechanics and its Applications, 2020, vol. 543, issue C

Abstract: Road separations, intersections, and crosswalks, which are important components of highways, are seen as significant areas for autonomous vehicles and advanced driver assistance systems because traffic accident occurrence rate is considerably high in these areas. In this study, an image processing method and a deep learning based approach on real images has been proposed in order to provide instant information for drivers and autonomous vehicles, or to develop warning systems as part of advanced driver assistance systems to prevent or minimize traffic accidents. The information is obtained from the classification of images belonging to the separations, intersections and crosswalks on the road using a new model and VggNet, AlexNet, LeNet based on Convolutional Neural Network(CNN). We have obtained high classification accuracy with our model based on CNN. The result of the study performed on different datasets showed that the proposed method is usable for driver assistance systems and an effective structure that can be used in many areas such as warning both vehicles and drivers.

Keywords: Road intersection detection; Crosswalk detection; Deep learning; Intelligent transportation systems (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:543:y:2020:i:c:s0378437119319582

DOI: 10.1016/j.physa.2019.123510

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