MVSR Normalization Algorithm Method for Improving Vehicle License Plate Recognition

Sertaç Yaman; Yavuz Erol

Makale detayları

MVSR Normalization Algorithm Method for Improving Vehicle License Plate Recognition

Yazarlar:

Sertaç Yaman | Yavuz Erol

Yayıncı:

Fırat University

Ana Başlıklar:

Neural Networks | Image Processing | Machine Vision

Dergi:

Turkish Journal of Science and Technology Vol.18 Issue 2, pp.543-552

Belge Tipi:

Makale

Yayın Tarihi:

1/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001290413

pISSN:

1308-9080

Anahtar kelimeler:

Özet:

 Image processing and embedded systems are used in many applications such as object recognition, robotic applications, autonomous and remote control systems developed for the defense industry, medical applications, face recognition, and vehicle license plate recognition. Many vehicle license number plate detection methods are not effective under vehicle license plate images have a degree of rotation or low resolution images. Thus, we used MVSR normalization algorithm to detect vehicle license plate recognition for better accuracy and lower computational cost. The MVSR normalization algorithm, MeanVariance-Softmax-Rescale processes respectively is applied for high-accuracy real-time vehicle license plate detection.

 Image processing and embedded systems are used in many applications such as object recognition, robotic applications, autonomous and remote control systems developed for the defense industry, medical applications, face recognition, and vehicle license plate recognition. Many vehicle license number plate detection methods are not effective under vehicle license plate images have a degree of rotation or low resolution images. Thus, we used MVSR normalization algorithm to detect vehicle license plate recognition for better accuracy and lower computational cost. The MVSR normalization algorithm, MeanVariance-Softmax-Rescale processes respectively is applied for high-accuracy real-time vehicle license plate detection.

 Image processing and embedded systems are used in many applications such as object recognition, robotic applications, autonomous and remote control systems developed for the defense industry, medical applications, face recognition, and vehicle license plate recognition. Many vehicle license number plate detection methods are not effective under vehicle license plate images have a degree of rotation or low resolution images. Thus, we used MVSR normalization algorithm to detect vehicle license plate recognition for better accuracy and lower computational cost. The MVSR normalization algorithm, MeanVariance-Softmax-Rescale processes respectively is applied for high-accuracy real-time vehicle license plate detection.

 Görüntü işleme ve gömülü sistemler; nesne tanıma, robotik uygulamalar, savunma sanayi için geliştirilen otonom ve uzaktan kontrol sistemleri, medikal uygulamalar, yüz tanıma, araç plaka tanıma gibi birçok uygulamada kullanılmaktadır. Dönme derecesine sahip veya düşük çözünürlüklü görüntülere sahip araç plaka görüntüleri altında birçok araç plakası tespit yöntemi etkili değildir. Bu nedenle, daha iyi doğruluk ve daha düşük hesaplama maliyeti için araç plakası tanımayı tespit etmek amacıyla MVSR normalleştirme algoritmasını kullandık. Yüksek doğruluklu, gerçek zamanlı araç plakası tespiti için sırasıyla MVSR normalleştirme algoritması, Ortalama-Varyans-Softmax-Yeniden Ölçeklendirme işlemleri uygulanmıştır.

Makale detayları

MVSR Normalization Algorithm Method for Improving Vehicle License Plate Recognition

Yazarlar:

Sertaç Yaman | Yavuz Erol

Yayıncı:

Fırat University

Ana Başlıklar:

Neural Networks | Image Processing | Machine Vision

Dergi:

Turkish Journal of Science and Technology Vol.18 Issue 2, pp.543-552

Belge Tipi:

Makale

Yayın Tarihi:

1/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001290413

pISSN:

1308-9080

MVSR algorithm

Vehicle License Plate Recognition

Normalization

Artificial neural network.MVSR algoritması

araç plaka tespiti

normalizasyon

yapay sinir ağı

Sayı

Makale detayları

Yazarlar: Sertaç Yaman | Yavuz Erol

Yayıncı: Fırat University

Ana Başlıklar: Neural Networks | Image Processing | Machine Vision

Dergi: Turkish Journal of Science and Technology Vol.18 Issue 2, pp.543-552

Belge Tipi: Makale

Yayın Tarihi: 1/2023

Belge Dili: İngilizce

Document ID (Org No.): 2001290413

pISSN: 1308-9080

Sayı

Yazarlar:

Sertaç Yaman | Yavuz Erol

Yayıncı:

Fırat University

Ana Başlıklar:

Neural Networks | Image Processing | Machine Vision

Dergi:

Turkish Journal of Science and Technology Vol.18 Issue 2, pp.543-552

Belge Tipi:

Makale

Yayın Tarihi:

1/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001290413

pISSN:

1308-9080