- Title
- Improving licence plate detection using generative adversarial networks
- Creator
- Boby, Alden
- Creator
- Brown, Dane L
- Subject
- To be catalogued
- Date Issued
- 2022
- Date
- 2022
- Type
- text
- Type
- article
- Identifier
- http://hdl.handle.net/10962/464145
- Identifier
- vital:76480
- Identifier
- xlink:href="https://link.springer.com/chapter/10.1007/978-3-031-04881-4_47"
- Description
- The information on a licence plate is used for traffic law enforcement, access control, surveillance and parking lot management. Existing li-cence plate recognition systems work with clear images taken under controlled conditions. In real-world licence plate recognition scenarios, images are not as straightforward as the ‘toy’ datasets used to bench-mark existing systems. Real-world data is often noisy as it may contain occlusion and poor lighting, obscuring the information on a licence plate. Cleaning input data before using it for licence plate recognition is a complex problem, and existing literature addressing the issue is still limited. This paper uses two deep learning techniques to improve li-cence plate visibility towards more accurate licence plate recognition. A one-stage object detector popularly known as YOLO is implemented for locating licence plates under challenging situations. Super-resolution generative adversarial networks are considered for image upscaling and reconstruction to improve the clarity of low-quality input. The main focus involves training these systems on datasets that include difficult to detect licence plates, enabling better performance in unfavourable conditions and environments.
- Format
- computer
- Format
- online resource
- Format
- application/pdf
- Format
- 1 online resource (12 pages)
- Format
- Publisher
- SpringerLink
- Language
- English
- Relation
- Iberian Conference on Pattern Recognition and Image Analysis
- Relation
- Boby, A. and Brown, D., 2022, April. Improving licence plate detection using generative adversarial networks. In Iberian Conference on Pattern Recognition and Image Analysis (pp. 588-601). Cham: Springer International Publishing
- Relation
- Iberian Conference on Pattern Recognition and Image Analysis p. 588 2022 1611-3349
- Rights
- Publisher
- Rights
- Use of this resource is governed by the terms and conditions of the SpringerLink Terms of Use Statement ( https://link.springer.com/termsandconditions)
- Rights
- Closed Access
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Collections
Brown, Dane A (Dr)
RU Department of Computer Science
SDG 09. Industry, Innovation and Infrastructure
| Thumbnail | File | Description | Size | Format | |||
|---|---|---|---|---|---|---|---|
| View Details Download | SOURCE1 | Improving licence plate detection using generative adversarial networks.pdf | 700 KB | Adobe Acrobat PDF | View Details Download |