Yolo Algoritması Kullanarak Derin Öğrenme Tabanlı Trafik İşareti Tanıma

Abstract

Trafik işaretleri tespiti nesne tanıma uygulamaları arasında son yıllarda oldukça fazla ilgi görmektedir. Trafik işaretlerinin doğru ve hızlı şekilde algılanması otonom araçlarda önemli bir teknik sorunu da ortadan kaldıracaktır. Gelişen yapay zeka teknolojiyle beraber derin öğrenme uygulamaları yüksek algılama ve doğru tespitle objeleri ayırt edebilmektedir. Yapay zeka teknolojisi kullanarak trafik levhalarının tespiti için bu alanda yeni uygulamalar test edilmektedir. Bu kapsamda bu makale trafik işaretlerini derin öğrenme algoritmalarıyla doğru tespit etmek için önemli bir yere sahiptir. Bu çalışmada trafik işaretlerinin tespiti için güncel bir algoritma olan YOLOv5’in en yeni üç modeli kullanılmıştır. Derin öğrenme algoritmalarını temel alan bir trafik işaret algılama ve tanıma sistemi önerilmiştir. Önerilen çalışmada aynı zamanda gerçek zamanlı levha tespiti de gerçekleştirilmiştir. Modellerin precision, recall ve mAP50 değerleri karşılaştırıldığında en yüksek sonuçlar sırasıyla %99.3, %95 ve %98.1 olarak elde edilmiştir. Deneysel sonuçlar YOLOv5 mimarilerinin hem görüntü hem de video ile nesne tespiti için doğru bir yöntem olduğunu desteklemektedir. YOLOv5 algoritmalarının trafik işaretlerini ve ortalama hassasiyeti (mAP) algılamada oldukça başarılı olduğu görülmüştür.

Keywords

• Derin öğrenme
• Trafik işareti tanıma
• Yolo
• Deep learning
• traffic sign recognition

Deep Learning Based Traffic Sign Recognition Using YOLO Algorithm

Abstract

Traffic sign detection has attracted a lot of attention in recent years among object recognition applications. Accurate and fast detection of traffic signs will also eliminate an important technical problem in autonomous vehicles. With the developing artificial intelligency technology, deep learning applications can distinguish objects with high perception and accurate detection. New applications are being tested in this area for the detection of traffic signs using artificial intelligence technology. In this context, this article has an important place in correctly detecting traffic signs with deep learning algorithms. In this study, three model of (You Only Look Once) YOLOv5, an up-to-date algorithm for detecting traffic signs, were used. A system that uses deep learning models to detect traffic signs is proposed. In the proposed study, real-time plate detection was also performed. When the precision, recall and mAP50 values of the models were compared, the highest results were obtained as 99.3, 95% and 98.1%, respectively. Experimental results have supported that YOLOv5 architectures are an accurate method for object detection with both image and video. It has been seen that YOLOv5 algorithms are quite successful in detecting traffic signs and average precession.

Keywords

• Deep learning
• traffic sign recognition
• Yolo

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