Detection Algorithm of Cascaded Adaboost License Plate Based on HSV color model and MB_LBP Features

Abstract:

License plate detection is an important part of the license plate recognition system, which deeply affects the accuracy of license plate recognition. A method for cascade adaboost license plate detection based on HSV color model and multi-block local binary patterns (MB_LBP) is presented to realize fast and accurate license plate detection and recognition. Firstly, the license plate image is transformed from RGB color space to HSV color space, and the ratio of the blue pixels to the total pixels of the license plate is counted to construct the first class strong classifier. Then, the MB_LBP feature is extracted from the license plate character samples, and the feature selection and the classifier training are carried out by using the Adaboost classifier training method. Finally, a new license plate detection algorithm is formed by using the Cascade structure detection method. Experiments results show that the license plate detector improves the detection rate and the detection speed.