License Plate Detection for Multi-national Vehicles: An Illumination Invariant Approach in Multi-lane Environment

Muhammad Rizwan Asif, Chun Qi, Tiexiang Wang, Muhammad Sadiq Fareed, Syed Ali Reza

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaperJournal articleResearchpeer-review


Only a few methods in literature are effective for multi-national license plate detection in a multi-lane scenario. These methods are prone to illumination variance, complex background and weak-edged license plates. In this paper, we propose a novel illumination invariant method to handle multi-national vehicle license plates of different colors and styles. Red corona is initially used to detect the tail-lights of vehicles to establish region-of-interest as the license plates are in a vicinity of its tail-lights. The vertical edges within each region-of-interest are obtained using a unique approach that preserve license plate edges for improved performance. Heuristic energy map is then used to distinguish the license plate area. To validate the detected regions, high-level features extracted from AlexNet Convolutional Neural Network are used. Extensive experiments on the license plates of six countries show that the proposed approach not only ensures real-time performance, but also outperforms the conventional and deep-learning methods.

Original languageEnglish
JournalComputers & Electrical Engineering
Pages (from-to)132-147
Number of pages16
Publication statusPublished - 16 Jul 2019
Externally publishedYes


  • Corona effect
  • Intelligent traffic system
  • License plate detection
  • Multi-national vehicles
  • Vehicle identification
  • Vehicle rear lights


Dive into the research topics of 'License Plate Detection for Multi-national Vehicles: An Illumination Invariant Approach in Multi-lane Environment'. Together they form a unique fingerprint.

Cite this