| dc.description.abstract | ABSTRACT
Automatic Number Plate Recognition (ANPR) systems are applied in many fields such
as automatic electronic toll collection, car park management and access control,
logistics and vehicle tracking, traffic law enforcement and crime resolution amongst
others. Motion blur, plate orientation, lighting changes and image noise severely lower
the detection speed and recognition accuracy of these systems. The incorporation of
machine learning algorithms in ANPRs has seen Convolutional Neural Network (CNN)
being used to develop ANPR models with improved performance in license plate
detection. CNNs are best suited for image data where the number of features is large
such as license plate detection. This is attributed to their design architecture which
enables them to perform feature extraction automatically. However, their speed of
execution is slow as the model has to learn a lot of features. Support Vector Machine
(SVM) is a supervised machine learning algorithm suitable for classification and
regression problems with datasets that have a small number of features. It doesn’t scale
up well for large datasets with many features. It has demonstrated high speed and
accuracy when used for classification in small datasets such as character recognition.
The final stage in ANPR is a character recognition phase and involves few features.
These two algorithms have been deployed independently, however the concept of
combining the two algorithms for ANPR models remains highly unexplored. The
research therefore combines the two models (CNN and SVM) to come up with an
efficient hybrid ANPR system with improved number plate recognition accuracy. The
two models were developed using a deep cascade framework; a CNN with a SoftMax
classifier and a hybrid CNN with a SVM classifier. The Universidade Federal do Paraná
(UFPR-ALPR) dataset was used to train validate and test the models. Recognition
accuracy, precision, recall and F1 score metrics were used to evaluate the model. The
hybrid CNN-SVM model had a recognition accuracy of 91.25% against 89.07 % from
the pure CNN model. The weighted average precision, recall, and F1-score of the
hybrid CNN-SVM was 92%, 91% and 91% respectively, which was better compared
to that of pure CNN. The hybrid model was tested for external validity using the Smart
Sense Laboratory (SSIG) dataset. The hybrid CNN-SVM model had a recognition
accuracy of 91% against 89 % from the pure CNN model. The weighted average
precision, recall, and F1 score of the hybrid CNN-SVM was 91%, 91% and 91%
respectively which was better compared to that of pure CNN, which had 90%, 89% and
89% respectively. | en_US |
