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- W4226059233 abstract "The information on a licence plate is used for traffic law enforcement, access control, surveillance and parking lot management. Existing licence 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 benchmark 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 licence 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." @default.
- W4226059233 created "2022-05-05" @default.
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- W4226059233 date "2022-01-01" @default.
- W4226059233 modified "2023-10-01" @default.
- W4226059233 title "Improving Licence Plate Detection Using Generative Adversarial Networks" @default.
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- W4226059233 doi "https://doi.org/10.1007/978-3-031-04881-4_47" @default.
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