Journals / Turkish Journal of Science and Technology / 2023 / Cilt: 18 Sayı: 2

MVSR Normalization Algorithm Method for Improving Vehicle License Plate Recognition

Araç Plaka Tespitinin İyileştirilmesine Yönelik MVSR Normalleştirme Algoritması Yöntemi

Pages
543–552
Publication Date
2023-09-01
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The MVSR normalization algorithm, Mean–Variance-Softmax-Rescale processes respectively is applied for high-accuracy real-time vehicle license plate detection.

📌 3 Key Takeaways

🎯
Objective & Hypothesis: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.
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Key Findings & Data:Many vehicle license number plate detection methods are not effective under vehicle license plate images have a degree of rotation or low resolution images.
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Conclusion & Impact:The MVSR normalization algorithm, Mean–Variance-Softmax-Rescale processes respectively is applied for high-accuracy real-time vehicle license plate detection.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:MVSR algoritması · araç plaka tespiti · normalizasyon

Abstract

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, Mean–Variance-Softmax-Rescale processes respectively is applied for high-accuracy real-time vehicle license plate detection.

Özet

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.

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Cite This Article

YAMAN, S., EROL, Y. (2023). MVSR Normalization Algorithm Method for Improving Vehicle License Plate Recognition. Turkish Journal of Science and Technology, Cilt: 18 Sayı: 2, 543–552. https://ajindex.com/articles/959791

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References (15)

  1. Nagare AP. License plate character recognition system using neural network. International Journal of Computer Applications 2011; 25(10): 36-39
  2. Zhai X, Bensaali F, Ramalingam S. Real-time license plate localisation on FPGA. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. Work., IEEE Computer Society 2011; 14–19.
  3. Patel C, Patel A, Patel D. Optical character recognition by open source OCR tool tesseract: A case study. International Journal of Computer Applications 2012; 55(10): 50-56.
  4. Du S, Ibrahim M, Shehata M, Badawy W. Automatic license plate recognition (ALPR): A state-of-the-art review. IEEE Trans. Circuits Syst. Video Technol. 2013; 23: 311–325.
  5. Massoud MA, Sabee M, Gergais M, Bakhit R. Automated new license plate recognition in Egypt. Alexandria Eng. J. 2013; 52(3): 319–326.
  6. Zang D, Chai Z, Zhang J, Zhang D, Cheng J. Vehicle license plate recognition using visual attention model and deep learning. Journal of Electronic Imaging 2015; 24(3): 033001. Zhai X, Bensaali F. Improved number plate character segmentation algorithm and its efficient FPGA implementation. J. Real-Time Image Process 2015; 10: 91–103.
  7. Fan R, Prokhorov V, Dahnoun N. Faster-than-real-time linear lane detection implementation using SoC DSP TMS320C6678. IST 2016 IEEE Int. Conf. Imaging Syst. Tech. Proc.; 2016; pp. 306–311.
  8. Calderon JAF, Vargas JS, Pérez-Ruiz A. License plate recognition for Colombian private vehicles based on an embedded system using the ZedBoard. 2016 IEEE Colombian Conference on Robotics and Automation (CCRA); 2016;
  9. Bogota, Colombia. pp. 1-6.
  10. Promrit P, Suntiamorntut W. Design and development of lane detection based on FPGA. 2017 14th International Joint Conference on Computer Science and Software Engineering (JCSSE); 2017;
  11. NakhonSiThammarat, Thailand. pp. 1-4.
  12. Li H, Wang P, You M, Shen C. Reading car license plates using deep neural networks. Image Vis. Comput. 2018; 72: 14–23.
  13. Xie L, Ahmad T, Jin L, Liu Y, Zhang S. A new CNN-based method for multi-directional car license plate detection. IEEE Trans. Intell. Transp. Syst. 2018; 19: 507– 517. Hendry, Chen RC. Automatic License plate recognition via sliding-window darknet-YOLO deep learning. Image Vis. Comput. 2019; 87: 47–56.
  14. Viju VR, Radha. License plate recognition based on K-means clustering algorithm. Intell. Syst. Ref. Libr. Springer 2019; pp. 23–29.
  15. Yousif BB, Ata MM, Fawzy N, Obaya M. Toward an optimized neutrosophic k-means with genetic algorithm for automatic vehicle license plate recognition. IEEE Acce