Rapid Color-Background License Plate Localization with Algorithm Implementation

This project focuses on rapid identification of license plates with colored backgrounds using computer vision techniques. The implementation employs color segmentation based on HSV/YCbCr color spaces to isolate potential plate regions, followed by morphological operations and contour analysis to filter candidate areas. Key functions include adaptive thresholding for illumination invariance and aspect ratio validation for false positive reduction. We've developed this solution to provide a convenient and efficient license plate localization method applicable across various scenarios including parking lots, roadside vehicle management, and traffic monitoring systems. The algorithm utilizes machine learning-based color classification and edge detection features to ensure accurate plate detection under varying lighting conditions. Through this open-source project, we aim to enhance traffic management efficiency and public safety by enabling fast, reliable vehicle identification. The code architecture supports real-time processing through optimized image preprocessing pipelines and multi-threaded region analysis. We appreciate the community's support and welcome contributions to further improve the system's accuracy and performance.