Abstract:
Automatic detection of license plate (LP) is to localize license plate region from an
image without human involvement. So far a number of methods have already been
introduced for automatic license plate detection (ALPD), but most of them do not
consider various hazardous image conditions that exist in many real driving situations.
Hazardous condition means an image can be a ected by rainy or foggy weather, may
have low contrast environments (such as, indoor and night), can be blurred, having other
objects in the background and may have horizontally tilted LP area. All these issues
create challenges in developing e ective ALPD method. In this thesis, we propose a new
ALPD method which can e ectively detect LP area from image in hazardous conditions.
In the proposed ALPD, several innovative steps are introduced for handling the inherited
issues of hazardous conditions. For rain removal, a novel method is applied that uses
frequency domain mask to lter rain streaks from an image. The proposed rain removal
technique performs better than the existing single-image-based rain removal approach
(Kang et al. 2012). A new contrast enhancement method with a statistical binarization
approach is introduced in the proposed ALPD for handling low contrast indoor, night,
blur and foggy images. For correcting tilted LP, a novel Radon transform based tilt
correction method is applied. To lter non-LP areas, a few unique approaches are used
which are based on image entropy and average horizontal counting. This new ALPD
method is tested on 850 car images having di erent hazardous conditions and achieves
satisfactory results in LP detection. We also compare the performance of the proposed
ALPD method with two existing state-of-the-art methods (Wen et al. 2011 and Hasan
et al. 2013). The proposed ALPD method shows best performance among them in
terms of detection rate and average running time.
