Graduation Semester and Year
2020
Language
English
Document Type
Thesis
Degree Name
Master of Science in Electrical Engineering
Department
Electrical Engineering
First Advisor
Michael T Manry
Abstract
Convolutional Neural Networks (CNN) comprise a deep learning technology which is widely used to perform image classification. In this research, we review CNN structures and explain how they can be used for finding license plates in vehicle images. We summarize how the standard CNN processes images into features and compare it to Region-Based Convolutional Neural Networks (R-CNN). After comparing their pros and cons, we decide to design a R-CNN to train our dataset for this project. We find that the one trained with the most training data has the highest testing accuracy. The first training network detector leads to the highest testing accuracy that can reach 0.963 after 10 training epochs. The second training network detector leads to the highest testing accuracy that can reach 0.988 after 20 training epochs.
Keywords
CNN, R-CNN
Disciplines
Electrical and Computer Engineering | Engineering
License
This work is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 4.0 International License.
Recommended Citation
Luan, Shengyi, "CAR PLATE DETECTION USING REGION-BASED CONVOLUTIONAL NEURAL NETWORKS" (2020). Electrical Engineering Theses. 365.
https://mavmatrix.uta.edu/electricaleng_theses/365
Comments
Degree granted by The University of Texas at Arlington