Vehicle Plate Number Localization Using Memetic Algorithms and Convolutional Neural Networks

This paper introduces the third enhanced version of a genetic algorithm-based technique to allow fast and accurate detection of vehicle plate numbers (VPLN) in challenging image datasets. Since binarization of the input image is the most important and difficult step in the detection of VPLN, a hybrid technique is introduced that fuses the outputs of three fast techniques into a pool of connected components objects (CCO) and hence enriches the solution space with more solution candidates. Due to the combination of the outputs of the three binarization techniques, many CCOs are produced into the output pool from which one or more sequences are to be selected as candidate solutions. The pool is filtered and submitted to a new memetic algorithm to select the best fit sequence of CCOs based on an objective distance between the tested sequence and the defined geometrical relationship matrix that represents the layout of the VPLN symbols inside the concerned plate prototype. Using any of the previous versions will give moderate results but with very low speed. Hence, a new local search is added as a memetic operator to increase the fitness of the best chromosomes based on the linear arrangement of the license plate symbols. The memetic operator speeds up the convergence to the best solution and hence compensates for the overhead of the used hybrid binarization techniques and allows for real-time detection especially after using GPUs in implementing most of the used techniques. Also, a deep convolutional network is used to detect false positives to prevent fake detection of non-plate text or similar patterns. Various image samples with a wide range of scale, orientation, and illumination conditions have been experimented with to verify the effect of the new improvements. Encouraging results with 97.55% detection precision have been reported using the recent challenging public Chinese City Parking Dataset (CCPD) outperforming the author of the dataset by 3.05% and the state-of-the-art technique by 1.45%.