Otomatik sürüş için derin öğrenme metodu kullanarak trafik işaretlerinin tespiti

Year 2023, Volume: 5 Issue: 3, 259 - 267, 13.10.2023

Hayati Akgün , Sinan Uğuz

https://doi.org/10.47933/ijeir.1358959

Abstract

Günümüzde sağlık, askeri, ekonomi ve üretim endüstrisi başta olmak üzere pek çok alanda kullanılan derin öğrenme uygulamaları yapay zekânın önemli bir alanını oluşturmaktadır. Otonom araç teknolojilerinin gelişiminde önemli bir payı bulunan nesne sınıflandırma ve nesne tanıma uygulamaları derin öğrenme çalışmalarının odak noktasını oluşturmaktadır. Hem araçlar hem de yayalar için güvenli sürüş noktasında derin öğrenme modellerine dayanan uygulamaların başarılı performanslar gösterdiği son yapılan çalışmalarda daha net olarak görülmektedir. Otonom sistemlerin güvenli sürüş için trafik işaretlerini yüksek doğruluk değerleri ile tanıması büyük önem taşımaktadır. Özellikle yaya geçidi, okul bölgesi, şehir içi hız limitleri en kritik trafik işaretleri arasında sayılabilir. Bu tez çalışmasında kendi imkânlarımızla elde ettiğimiz trafik işaretlerinden oluşan veri seti kullanılarak önemli nesne tanıma mimarilerinden olan faster R-CNN ile eğitimler gerçekleştirilmiştir. Çalışma neticesinde ortaya konan donanımsal modül sayesinde aracın sürücüsünü sesli ikazlar ile uyaran bir sistem geliştirilmiştir. Geliştirilen donanımsal modül hız limitlerinin yanı sıra yaya geçidi ve okul bölgesi gibi trafik işaretçilerini tespit ederek gerçek zamanlı olarak sürücüyü uyarabilmektedir. Ayrıca geliştirilen yazılım için Python dili kullanılırken, veri seti eğitimleri Tensorflow kütüphanesi kullanılarak gerçekleştirilmiştir. Çalışmanın otonom araç uygulamalarında trafik işaretlerinin tanınması noktasında bir katkı sağlayacağı düşünülmektedir.

Keywords

cnn, nesne tespiti, trafik işareti tespiti

Project Number

This work was supported by Research Fund of Isparta University of Applied Sciences. Project Number: 2019-YL1-0004

DETECTION OF TRAFFIC SIGNS FOR AUTONOMOUS DRIVING WITH THE DEEP LEARNING METHOD

Abstract

Deep learning practices used in many fields, in particular, in health, military, economy, and production industries, are an important area of artificial intelligence in our age. The object classification and object recognition applications, which play a significant role in the development of autonomous vehicle technologies, constitute the focal point of the deep learning studies. It is clear that the recent studies based on the deep learning models show that they are useful and successful performances for safe driving not only for vehicles but also for pedestrians. It is very crucial and significant that the autonomous systems recognize the traffic signs with high accuracy for a safe driving. Especially, the pedestrian crossing, school district, urban speed limits can be regarded among the most critical traffic signs. In this study, we have used the data set including the traffic signs obtained by our own means to carry out trainings by using faster R-CNN which is regarded as one of the most important recognition architectures. Thanks to the hardware module produced as a result of the operation, we have developed a system that warns the driver of the vehicle with audible warning. The developed hardware module can detect not only the speed limits, traffic signs but also pedestrian crossings and school districts and alert the driver in reel-time. The developed hardware module is based on Arduino and because of the GPS sensor, it can also show the speed of the vehicle. Moreover, we have used Python for the developed software and the dataset trainings have been carried out by using the Tensorflow library. We think that the study will contribute a lot to the recognition of traffic signs for the autonomous vehicle applications.

Keywords

cnn, traffic signs recognition, object detection

Project Number

This work was supported by Research Fund of Isparta University of Applied Sciences. Project Number: 2019-YL1-0004

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