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AKILLI SİSTEMLER VE UYGULAMALARI DERGİSİ
JOURNAL OF INTELLIGENT SYSTEMS WITH APPLICATIONS
J. Intell. Syst. Appl.
E-ISSN: 2667-6893
Creative Commons License This work is licensed under a Creative Commons Attribution 4.0 International License.

A Convolutional Neural Network Model for Road Flow Direction Detection

Yol Akış Yönünün Tespiti için Bir Konvolüsyonel Sinir Ağı Modeli

How to cite: Tümen V, Yıldırım , Ergen B. A convolutional neural network model for road flow direction detection. Akıllı Sistemler ve Uygulamaları Dergisi (Journal of Intelligent Systems with Applications) 2019; 2(2): 94-99. DOI: 10.54856/jiswa.201912072

Full Text: PDF, in Turkish.

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Title: A Convolutional Neural Network Model for Road Flow Direction Detection

Abstract: 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.

Keywords: Deep learning; image processing; Road direction detection; road flow detection; classification


Başlık: Yol Akış Yönünün Tespiti için Bir Konvolüsyonel Sinir Ağı Modeli

Özet: Sürücüsüz araçlar gibi yapay zekanın etkin olarak kullanıldığı kritik alanlarda araçların hareket halinde olduğu yola ait özelliklerin gerçek zamanlı olarak tespit edilmesi önemli bir çalışma alanıdır. Bu makale çalışmasının amacı, hareket halindeki bir aracın yolun akış yönünü tespit etmesini sağlayacak bir derin öğrenme yöntemi sunmaktır. Çalışmada, Yol Akış Yönü (YAY) tespiti için derin öğrenme modellerinden Konvolüsyonel Sinir Ağları (KSA) kullanılmıştır. Akış yönünün tespiti için geliştirilen YAYKSA modeli CMU VASC görüntü veri tabanında bulunan 587 adet gerçek yol resimleri üzerinde uygulanmıştır. Hazırlanan modelin başarımlarını kıyaslamak için aynı veriler üzerinde, yaygın KSA modeli olan Cifar modeli uygulanmıştır. Elde edilen sınıflandırma sonuçlarına göre, tasarlanan YAY-KSA modelinin %80.1 düzeyinde akış yönünü doğru olarak tespit ettiği görülmüştür.

Anahtar kelimeler: Derin öğrenme; görüntü işleme; yol yönü tespiti; yol akışı tespiti; sınıflandırma


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