- Title
- Enhancing licence plate recognition for a robust vehicle re-identification system
- Creator
- Boby, Alden Zachary
- ThesisAdvisor
- Brown, Dane
- ThesisAdvisor
- Connan, James
- Subject
- Automobile theft South Africa
- Subject
- Deep learning (Machine learning)
- Subject
- Object detection
- Subject
- YOLOv7
- Subject
- YOLO
- Subject
- Pattern recognition systems
- Subject
- Image processing Digital techniques
- Subject
- Automobile license plates
- Date
- 2024-10-11
- Type
- Academic theses
- Type
- Master's theses
- Type
- text
- Identifier
- http://hdl.handle.net/10962/464322
- Identifier
- vital:76501
- Description
- Vehicle security is a growing concern for citizens of South Africa. Law enforcement relies on reports and security camera footage for vehicle identification but struggles to match the increasing number of carjacking incidents and low vehicle recovery rates. Security camera footage offers an accessible means to identify stolen vehicles, yet it often poses hurdles like anamorphic plates and low resolution. Furthermore, depending on human operators proves inefficient, requiring faster processes to improve vehicle recovery rates and trust in law enforcement. The integration of deep learning has revolutionised object detection algorithms, increasing the popularity of vehicle tracking for security purposes. This thesis investigates advanced deep-learning methods for a comprehensive vehicle search and re-identification system. It enhances YOLOv7’s algorithmic capabilities and employs preprocessing techniques like super-resolution and perspective correction via the Improved Warped Planar Object Detection network for more effective licence plate optical character recognition. Key contributions include a specifically annotated dataset for training object detection models, an optical character recognition model based on YOLOv7, and a method for identifying vehicles in unrestricted data. The system detected rectangular and square licence plates without prior shape knowledge, achieving a 98.7% character recognition rate compared to 95.31% in related work. Moreover, it outperformed traditional optical character recognition by 28.25% and deep-learning EasyOCR by 14.18%. Its potential applications in law enforcement, traffic management, and parking systems can improve surveillance and security through automation.
- Description
- Thesis (MSc) -- Faculty of Science, Computer Science, 2024
- Format
- computer, online resource, application/pdf, 1 online resource (151 pages), pdf
- Publisher
- Rhodes University, Faculty of Science, Computer Science
- Language
- English
- Rights
- Boby, Alden Zachary
- Rights
- Use of this resource is governed by the terms and conditions of the Creative Commons "Attribution-NonCommercial-ShareAlike" License (http://creativecommons.org/licenses/by-nc-sa/2.0/)
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| Thumbnail | File | Description | Size | Format | |||
|---|---|---|---|---|---|---|---|
| View Details | SOURCE1 | BOBY-MSC-TR24-156.pdf | 2 MB | Adobe Acrobat PDF | View Details |