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Vehicle Plate Number Localization Using Memetic Algorithms and Convolutional Neural Networks

Computers, Materials & Continua 2023年第2期74卷 3539-3560页

作者： gibrael abosamra Faculty of Computing and Information Technology King Abdulaziz UniversityJeddah21589Saudi Arabia

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 *** 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 *** 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 *** 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 *** any of the previous versions will give moderate results but with very low ***,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 *** 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 ***,a deep convolutional network is used to detect false positives to prevent fake detection of non-plate text or similar *** image samples with a wide range of scale,orientation,and illumination conditions have been experimented with to verify the effect of the new *** 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

关键词： Genetic algorithms memetic algorithm convolutional neural network object detection adaptive binarization filters license plate detection

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An Optimized Deep Residual Network with a Depth Concatenated Block for Handwritten Characters Classification

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Computers, Materials & Continua 2021年第7期68卷 1-28页

作者： gibrael abosamra Hadi Oqaibi Department of Information Systems King Abdulaziz UniversityJeddah21589SA

Even though much advancements have been achieved with regards to the recognition of handwritten characters,researchers still face difficulties with the handwritten character recognition problem,especially with the advent of new datasets like the Extended Modified National Institute of Standards and Technology dataset(EMNIST).The EMNIST dataset represents a challenge for both machine-learning and deep-learning techniques due to inter-class similarity and intra-class ***-class similarity exists because of the similarity between the shapes of certain characters in the *** presence of intra-class variability is mainly due to different shapes written by different writers for the same *** this research,we have optimized a deep residual network to achieve higher accuracy *** published state-of-the-art *** approach is mainly based on the prebuilt deep residual network model ResNet18,whose architecture has been enhanced by using the optimal number of residual blocks and the optimal size of the receptive field of the first convolutional filter,the replacement of the first max-pooling filter by an average pooling filter,and the addition of a drop-out layer before the fully connected layer.A distinctive modification has been introduced by replacing the final addition layer with a depth concatenation layer,which resulted in a novel deep architecture having higher accuracy *** pure residual ***,the dataset images’sizes have been adjusted to optimize their visibility in the ***,by tuning the training hyperparameters and using rotation and shear augmentations,the proposed model outperformed the state-of-the-art models by achieving average accuracies of 95.91%and 90.90%for the Letters and Balanced dataset sections,***,the average accuracies were improved to 95.9%and 91.06%for the Letters and Balanced sections,respectively,by using a group of 5 instances of the trained models and averagin

关键词： Handwritten character classification deep convolutional neural networks residual networks GoogLeNet ResNet18 DenseNet drop-out L2 regularization factor learning rate

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