Open AccessA Convolutional Neural Network Model for Road Flow Direction Detection
Author(s) -
Vedat Tümen,
Özal Yıldırım,
Burhan Ergen
Publication year - 2019
Publication title -
akıllı sistemler ve uygulamaları dergisi
Language(s) - English
Resource type - Journals
ISSN - 2667-6893
DOI - 10.54856/jiswa.201912072
Subject(s) - convolutional neural network , computer science , artificial intelligence , flow (mathematics) , artificial neural network , optical flow , motion (physics) , path (computing) , computer vision , deep learning , work (physics) , traffic flow (computer networking) , image (mathematics) , pattern recognition (psychology) , mathematics , engineering , geometry , mechanical engineering , computer security , programming language
It is an important work area to determine realtime characteristics of roads where vehicles are in motion in critical areas where artificial intelligence is effectively used, such as driverless vehicles. The purpose of this article work is to present a deeper learning method that will allow a vehicle in motion to detect the direction of flow in the path. Convolutional Neural Networks (KSA) have been used as deep learning models for the determination of the direction of flow (YAY) in the study. The YAY-KSA model developed for flow direction detection is applied on 587 real road images in the CMU VASC image database. To compare the performances of the prepared model, Cifar model which is a common KSA model was applied on the same data. According to the classification results obtained, it was seen that the designed YAY-KSA model correctly determined flow direction at 80.1% level.