Multi-national and Multi-language License Plate Detection using Convolutional Neural Networks

Salemdeeb M., ERTÜRK S.

ENGINEERING TECHNOLOGY & APPLIED SCIENCE RESEARCH, cilt.10, sa.4, ss.5979-5985, 2020 (ESCI) identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 10 Sayı: 4
  • Basım Tarihi: 2020
  • Dergi Adı: ENGINEERING TECHNOLOGY & APPLIED SCIENCE RESEARCH
  • Derginin Tarandığı İndeksler: Emerging Sources Citation Index (ESCI)
  • Sayfa Sayıları: ss.5979-5985
  • Kocaeli Üniversitesi Adresli: Evet

Özet

Many real-life machine and computer vision applications are focusing on object detection and recognition. In recent years, deep learning-based approaches gained increasing interest due to their high accuracy levels. License Plate (LP) detection and classification have been studied extensively over the last decades. However, more accurate and language-independent approaches are still required. This paper presents a new approach to detect LPs and recognize their country, language, and layout. Furthermore, a new LP dataset for both multinational and multi-language detection, with either one-line or two-line layouts is presented. The YOLOv2 detector with ResNet feature extraction core was utilized for LP detection, and a new low complexity convolutional neural network architecture was proposed to classify LPs. Results show that the proposed approach achieves an average detection precision of 99.57%, whereas the country, language, and layout classification accuracy is 99.33%.