Automatic license plate detection in hazardous condition

•License plate detection in hazardous condition.•Five novel contributions.•Compared with few existing approaches.•Experimented using two image databases. Automatic detection of license plate (LP) is to localize a license plate region from an image without human involvement. So far a number of method...

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Bibliographic Details
Published in:Journal of visual communication and image representation 2016-04, Vol.36, p.172-186
Main Authors: Azam, Samiul, Islam, Md Monirul
Format: Article
Language:English
Subjects:
Automatic license plate detection
Automation
Entropy
Fog affected
Fourier transform
Frequency domains
Hazardous
Hazardous condition
Horizontally tilted
Image contrast
Image detection
Low contrast image
Radon transform
Rain
Rain streaks
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Summary:•License plate detection in hazardous condition.•Five novel contributions.•Compared with few existing approaches.•Experimented using two image databases. Automatic detection of license plate (LP) is to localize a license plate region from an image without human involvement. So far a number of methods have 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 image condition means an image can have rainy or foggy weather effects, low contrast environments, objects similar to LP in the background, and horizontally tilted LP area. All these issues create challenges in developing effective ALPD method. In this paper, we propose a new ALPD method which effectively detects LP area from an image in the hazardous conditions. For rain removal we apply a novel method that uses frequency domain mask to filter rain streaks from an image. A new contrast enhancement method with a statistical binarization approach is introduced in the proposed ALPD for handling low contrast indoor, night, blurry and foggy images. For correcting tilted LP, we apply Radon transform based tilt correction method for the first time. To filter non-LP regions, a new condition is used which is based on image entropy. We test the proposed ALPD method on 850 car images having different hazardous conditions, and achieve satisfactory results in LP detection.
ISSN:1047-3203
1095-9076
DOI:10.1016/j.jvcir.2016.01.015