Derin Öğrenme ile Trafik İşareti Tanıma Algoritmasının Gerçek Zamanlı Uygulaması

Yıl 2022, Cilt: 3 Sayı: 2, 275 - 289, 18.12.2022

Faruk Emre Aysal , Kasım Yıldırım , Enes Cengiz

https://doi.org/10.55546/jmm.1196409

Cited By: 3

Öz

Otonom taşıtlar, otomotiv teknolojisinde popülaritesi giderek artan uygulama alanlardan biridir. Bu taşıtlar, iletişim, koordinasyon ve otonom sürüş yeteneğine sahip olmasıyla, ulaşım sistemlerini iyileştirmede önemli potansiyeller göstermektedir. İnsan müdahalesi olmadan kaynaktan hedefe hareket eden bu taşıtlar, kazalar ve trafik sıkışıklığı gibi insanların trafikte neden olduğu çeşitli sorunlara çözüm olarak ortaya çıktığı görülmektedir. Trafik kazaları ve trafik sıkışıklıkları büyük oranda sürücü kusurlarından ve trafik kurallarına uyulmamasından kaynaklanmaktadır. Bu nedenle, otonom taşıtlara yapay zekâ (AI) tabanlı sistemlerin entegre edilmesinin sosyal hayatta problem olarak görülen bu gibi durumlara çözüm olacağı öngörülmektedir. Literatüre bakıldığında VGGNet, ResNet50, MobileNetV2, NASNetMobile, İleri Beslemeli Sinir Ağları (Feed Forward Neural Network), Yinelemeli Sinir Ağları (Recurrent Neural network), Uzun Kısa Süreli Bellek (Long-Short Term Memory) ve Kapı Yinelemeli Birimler (Gated Recurrent Units) gibi derin öğrenme modellerinin yaygın bir şekilde trafik işareti sınıflandırma çalışmalarında kullanıldığı görülmektedir. Önceki araştırmalardan farklı olarak bu çalışmada, açık kaynak bir veri seti ile YOLOv5 versiyonlarının modelleri kullanılarak trafik işaret ve işaretçilerinin algılanması üzerine bir derin öğrenme uygulaması yapılmıştır. Özgün veri seti hazırlanarak çalışmada kullanılmıştır. Bu veri setinin farklı AI modellerine uygun olarak etiketleme işlemi tamamlanmıştır. Geliştirilen CNN modellerinde 15 farklı trafik işaret levha sınıfını içeren veri setinin eğitim işlemi gerçekleştirilmiştir. Bu modellerin sonuçları sistematik olarak karşılaştırılmış ve hiper parametre değişiklikleri ile modellerden optimum performans değerleri elde edilmiştir. Performans sonuçları incelendiğinde her bir sınıf için %98-99 oranında tespit başarısı elde edilmiştir.

Anahtar Kelimeler

Derin Öğrenme, Otonom Taşıt, Gerçek Zamanlı Uygulama, YOLOv5

Real-Time Application of Traffic Sign Recognition Algorithm with Deep Learning

Öz

Autonomous vehicles are one of the increasingly widespread application areas in automotive technology. These vehicles show significant potential in improving transportation systems, with their ability to communicate, coordinate and drive autonomously. These vehicles, which move from source to destination without human intervention, appear to be a solution to various problems caused by people in traffic, such as accidents and traffic jams. Traffic accidents and traffic jams are largely due to driver faults and non-compliance with traffic rules. For this reason, it is predicted that integrating artificial intelligence (AI)-based systems into autonomous vehicles will be a solution to such situations, which are seen as a problem in social life. Looking at the literature, VGGNet, ResNet50, MobileNetV2, NASNetMobile, Feed Forward Neural Networks, Recurrent Neural Networks, Long-Short Term Memory, and Gate Recurrent Units It is seen that deep learning models such as these are widely used in traffic sign classification studies. Unlike previous studies, in this study, a deep learning application was made for the detection of traffic signs and markers using an open-source data set and models of YOLOv5 versions. The original data set was prepared and used in the study. Labeling of this data set in accordance with different AI models has been completed. In the developed CNN models, the training process of the data set containing 15 different traffic sign classes was carried out. The results of these models were systematically compared, and optimum performance values were obtained from the models with hyperparameter changes. Real-time application was made using the YOLOv5s model. As a result, a success rate of 98-99% was achieved.

Anahtar Kelimeler

Deep Learning, Autonomous Vehicle, Real-Time Application, YOLOv5

Kaynakça

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