| License Plate Recognition (LPR) system is an important part of the Intelligent Transportation System (ITS), and plays an important role in modern traffic management and social security. License Plate Detection is one of the key technologies in LPR systems. With the extensive application of LPR systems, the algorithms used to work under controlled conditions and simple scenes is unable to fulfill the requirement. Therefore, license plate detection under complex scenes and imaging conditions becoming a research focus.In this thesis, license plate detection under complex scenes and imaging conditions is investigated, contributions are listed below.First, an improved heuristic license plate detection algorithm has been realized. In this algorithm, edge extraction is performed first according to the characteristics of regions contain license plate that they have a high density of edge information. By connecting the edges, several candidates are generated. The license plate was obtained by filtering these candidates with geometry features, edge distribution and color.Second, an algorithm based on Adaboost has been implemented. A kind of rectangle feature is selected, which indicates the percentage of the sum of a local area in the whole region. A cascade classifier with 85 features was trained by CS-Adaboost algorithm, and then a license plate detection algorithm was implemented. An experiment on a test set contains 583 images is carried out, the result and performance analysis was presented.Since the slant of the license plate would affect the character segmentation and other subsequent operation, a slant correction algorithm was implemented based on the hurdle model and tubiform interspace projection model. The result of experiment was presented.Finally, a license plate detection system was implemented by transplant the heuristic algorithm to DSP platform. This system can perform license plate detection on CIF images at the speed of 10 f/s. The result of experiment was reported. |
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