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Dergiler / International Journal of Applied Mathematics Electronics and Computers / 2026 / Volume: 7 Issue: 3

Vehicle Brand Detection Using Deep Learning Algorithms

Mehmet Furkan Kunduracı0000-0002-5888-4094
Humar Kahramanli Örnek0000-0003-2336-7924
Sayfa
—
Yayın Tarihi
2019-09-30
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Yapay Zeka Hızlı Bakış & Çıkarımlar

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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

It is observed that Faster-RCNN method performs 67.66% classification accuracy.

📌 3 Maddede Bu Makale

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Amaç & Hipotez:Today, information technologies are used in almost every stage of life.
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Temel Bulgular & Veri:It seeks to find solutions too many issues and problems.
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Sonuç & Katkı:It is observed that Faster-RCNN method performs 67.66% classification accuracy.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Vehicle brand detection · Image Processing · Deep Neural Networks

Abstract

Today, information technologies are used in almost every stage of life. It seeks to find solutions too many issues and problems. Image processing applications have been widely used in many areas in recent years and are trying to solve problems. Many applications which perform tasks such as classification, counting, measurement, target tracking have been developed. The aim of this study is to provide a solution for different applications using an effective and cost-effective method to detect the brand and model of vehicles. A classification method is implemented using deep neural network in the determination of the vehicle brand. The proposed solution is tested on various images taken from different angles and obtained from different sources. Faster-RCNN method which is one of deep neural networks is used to brand detection of vehicles in this study. It is observed that Faster-RCNN method performs 67.66% classification accuracy.

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Bu Makaleye Atıf Yap

Kunduracı, M. F., Örnek, H. K. (2019). Vehicle Brand Detection Using Deep Learning Algorithms. International Journal of Applied Mathematics Electronics and Computers, Volume: 7 Issue: 3. https://doi.org/10.18100/ijamec.578497

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Kaynakça (16)

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