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A New U-Net Based License Plate Enhancement Model in Night and Day Images

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12046)

Abstract

A new trend of smart city development opens up many challenges. One such issue is that automatic vehicle driving and detection for toll fee payment in night or limited light environments. This paper presents a new work for enhancing license plates captured in limited or low light conditions such that license plate detection methods can be expanded to detect images at night. Due to the popularity of Convolutional Neural Network (CNN) in solving complex issues, we explore U-Net-CNN for enhancing contrast of license plate pixels. Since the difference between pixels that represent license plates and pixels that represent background is too due to low light effect, the special property of U-Net that extracts context and symmetric of license plate pixels to separate them from background pixels irrespective of content. This process results in image enhancement. To validate the enhancement results, we use text detection methods and based on text detection results we validate the proposed system. Experimental results on our newly constructed dataset which includes images captured in night/low light/limited light conditions and the benchmark dataset, namely, UCSD, which includes very poor quality and high quality images captured in day, show that the proposed method outperforms the existing methods. In addition, the results on text detection by different methods show that the proposed enhancement is effective and robust for license plate detection.

Keywords

Text detection License plate detection U-Net Convolutional neural networks Low quality license plate detection 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Computer Vision and Pattern Recognition UnitIndian Statistical Institute, KolkataKolkataIndia
  2. 2.Faculty of Computer Science and Information TechnologyUniversity of MalayaKuala LumpurMalaysia
  3. 3.Faculty of Information Technology and Electrical Engineering, IIKNTNUGjøvikNorway
  4. 4.National Key Lab for Novel Software TechnologyNanjing UniversityNanjingChina
  5. 5.Faculty of Engineering and Information TechnologyUniversity of Technology, SydneyUltimoAustralia

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