Classification of Traffic Signs Using Transfer Learning Methods

Year 2024, , 829 - 838, 20.08.2024

Ömer Aykılıç , Muhammet Sinan Başarslan , Fatih Bal

https://doi.org/10.35414/akufemubid.1420978

Abstract

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.

Keywords

Traffic sign images, image processing, classification, transfer learning

Transfer Öğrenme Yöntemleri Kullanılarak Trafik İşaretlerinin Sınıflandırılması

Abstract

Ulaşım, insanların veya araçların bir yerden başka bir yere hareketine dayanan bir süreci ifade eder. Deniz yolları ve karayolları yüzyıllardır var olmuştur. Genellikle insanların günlük yaşamında, ticaretinde ve endüstriyel faaliyetlerinde çok önemli bir rol oynarlar. Bir ulaşım şekli olan karayolu, dünya genelinde ilk tercih edilen ulaşım şeklidir. Ancak karayollarında yaşanan kaosu önlemek için yetkililer tarafından çeşitli işaretler ve kurallar belirlenmiştir. Trafik işaretleri bu kuralların en önemlisidir. Bu çalışmada, transfer öğrenme modelleri (VGG16, VGG19, Xception ve EfficientNet) son teknoloji bir trafik işaretleri veri kümesi (German Traffic Sign Detection Benchmark-GTSDB) kullanılarak trafik işaretlerini sınıflandırmak için kullanılmıştır. Sınıflandırma değerlendirme kriteri olarak doğruluk kullanılmıştır. Çalışma için tasarlanan CNN modeli %98 doğruluk oranı ile en iyi sonucu vermiş ve literatürle yarışan bir model önerilmiştir.

Keywords

Trafik işareti görüntüleri, görüntü işleme, sınıflandırma, transfer öğrenme

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