MATLAB Implementation of License Plate Localization Algorithm

License plate localization is one of the key technologies in intelligent transportation systems, primarily using image processing methods to accurately identify vehicle license plate positions within complex backgrounds. MATLAB-based implementations typically utilize color segmentation techniques to enhance the salience of license plate regions. The implementation approach consists of three core stages: First is the preprocessing phase, where original images undergo noise reduction and contrast enhancement through median filtering and histogram equalization operations. Second involves color space conversion, transforming RGB images to HSV or YIQ color spaces that are more suitable for license plate color analysis, facilitating subsequent blue/yellow plate feature extraction. The critical color threshold segmentation phase follows, where based on Chinese license plate color characteristics (such as blue background with white text or yellow background with black text), threshold ranges are set in specific color channels. Candidate regions are obtained through pixel screening, requiring iterative parameter tuning for optimal segmentation results. Finally, morphological processing and region screening are performed using opening and closing operations to eliminate small noise points and connect adjacent regions. License plate candidates are then filtered based on geometric characteristics (aspect ratio, area, etc.), often combined with edge detection and projection analysis to improve localization accuracy. For beginners, understanding color space conversion principles and license plate feature variations across different color spaces is crucial for achieving stable localization. Algorithm optimization can incorporate texture features or deep learning methods to enhance localization robustness in complex environments. Code Implementation Details: - Preprocessing: Use medfilt2() for noise reduction and histeq() for contrast enhancement - Color Conversion: Employ rgb2hsv() or custom RGB to YIQ conversion functions - Thresholding: Implement imbinarize() with color-channel specific thresholds - Morphological Operations: Apply imopen() and imclose() with structuring elements - Region Analysis: Utilize regionprops() for geometric characteristic validation - Advanced techniques may incorporate edge() for Canny edge detection and projection analysis via sum(region,1) and sum(region,2)