Open AccessVehicle license plate recognition for fog‐haze environments
Author(s) -
Jin Xianli,
Tang Ruocong,
Liu Linfeng,
Wu Jiagao
Publication year - 2021
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/ipr2.12103
Subject(s) - license , haze , computer science , artificial intelligence , convolutional neural network , computer vision , convolution (computer science) , artificial neural network , geography , meteorology , operating system
The technique of vehicle license plate recognition can recognize and count the vehicles automatically, and thus many applications regarding the vehicles are greatly facilitated. However, the recognitions of vehicle license plates are extremely difficult especially in some fog‐haze environments because the fog and haze blur the boundaries and characters of license plates significantly, which makes the license plates hard to be detected or recognised. To this end, this paper proposes a vehicle License Plate Recognition method for Fog‐Haze environments (LPRFH). In LPRFH, a dark channel prior algorithm based on the local estimation of atmospheric light value is applied to dehaze the blurred images preliminarily. Then, the images are further dehazed, and the license plate regions are detected through a Joint Further‐dehazing and Region‐extracting Model on basis of an object detection convolution neural network. Finally, the image super‐resolution is accomplished with a convolution‐enhanced super‐resolution convolutional neural network, and hence the characters of license plates can be recognised successfully. Extensive experiments have been conducted, and the results indicate that LPRFH can recognise the license plates accurately even in some severe fog‐haze environments.