Yapay Zekâ Kullanilarak Trafi̇k İşaret Levhalarinin Siniflandirilmasi: Deni̇zli̇ İl Merkezi̇ İçi̇n Örnek Bi̇r Uygulama

Bayrakçı, Hilmi Cenk; PALANDIZ, Tolga; Özkahraman, Merdan

doi:10.46519/ij3dptdi.1021837

International Journal of 3D Printing Technologies and Digital Industry

2021

DOI: 10.46519/ij3dptdi.1021837

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Yapay Zekâ Kullanilarak Trafi̇k İşaret Levhalarinin Siniflandirilmasi: Deni̇zli̇ İl Merkezi̇ İçi̇n Örnek Bi̇r Uygulama

Hilmi Cenk Bayrakçı¹,

Tolga PALANDIZ²,

Merdan Özkahraman³

Abstract: Günümüzde sürekli olarak ilerlemekte olan teknolojik gelişmeler ile yapay zeka hayatımızın vazgeçilmez bir parçası haline gelmiştir. Yapay sinir ağlarının kullanıldığı çalışma alanlarından birisi de ulaşımdır. Ulaşım alanında olası kazaların azaltılması amacıyla sürücü destek sistemleri uygulamalarında yapay zeka kullanılmaktadır. Bu çalışmada hem trafik işaret levhalarının fotoğraflarının çekilmesiyle bireysel olarak oluşturulan veri seti hem de açık kaynak erişimli internet sitesinden (kaggle.com) elde edile… Show more

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Classification of Traffic Signs Using Transfer Learning Methods

Aykılıç,

Başarslan,

Bal

2024

Afyon Kocatepe University Journal of Sciences and Engineering

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Transportation refers to a process based on the movement of people or vehicles from one place to another. Sea routes and roads have existed for centuries. They generally play a very important role in people's daily life, trade and industrial activities. Highway, a mode of transportation, is the first preferred mode of transportation worldwide. However, various signs and rules have been set by the authorities to prevent chaos on the highways. Traffic signs are the most important of these rules. In this study, transfer learning models (VGG16, VGG19, Xception and EfficientNet) are used to classify traffic signs using a state-of-art traffic signs dataset (German Traffic Sign Detection Benchmark-GTSDB). Accuracy was used as the classification evaluation criterion. The CNN model designed for the study gave the best result with an accuracy rate of 98% and a model competing with the literature was proposed.

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Classification of Traffic Signs Using Transfer Learning Methods

Aykılıç,

Başarslan,

Bal

2024

Afyon Kocatepe University Journal of Sciences and Engineering

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

Aysal

Yildirim

Cengiz

2022

Journal of Materials and Mechatronics: A

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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.

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Yapay Zekâ Kullanilarak Trafi̇k İşaret Levhalarinin Siniflandirilmasi: Deni̇zli̇ İl Merkezi̇ İçi̇n Örnek Bi̇r Uygulama

Cited by 2 publications

References 10 publications

Classification of Traffic Signs Using Transfer Learning Methods

Classification of Traffic Signs Using Transfer Learning Methods

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

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