TY - JOUR
T1 - License Plate Detection for Multi-national Vehicles: An Illumination Invariant Approach in Multi-lane Environment
AU - Asif, Muhammad Rizwan
AU - Qi, Chun
AU - Wang, Tiexiang
AU - Sadiq Fareed, Muhammad
AU - Ali Reza, Syed
PY - 2019/9
Y1 - 2019/9
N2 - 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.
AB - 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.
KW - Corona effect
KW - Intelligent traffic system
KW - License plate detection
KW - Multi-national vehicles
KW - Vehicle identification
KW - Vehicle rear lights
UR - http://www.scopus.com/inward/record.url?scp=85068925132&partnerID=8YFLogxK
U2 - 10.1016/j.compeleceng.2019.07.012
DO - 10.1016/j.compeleceng.2019.07.012
M3 - Journal article
SN - 0045-7906
VL - 78
SP - 132
EP - 147
JO - Computers & Electrical Engineering
JF - Computers & Electrical Engineering
ER -